A novel multimedia device ability matching technique for ubiquitous computing environments

Abstract

In wireless multimedia sensor networks (WMSNs), wirelessly interconnected devices are able to ubiquitously retrieve multimedia contents such as video and audio streams from the environment. However, since WMSN applications are large-scale, dynamic and highly concurrent, how to achieve both effective multimedia device resource management and collaborative task scheduling simultaneously becomes a serious problem. In this paper, using the hierarchical modeling technique, we first propose a device ability model including spatial information. In order to solve the problem of insufficient capacity of single devices, we then give a composite device ability model and relevant calculation formulae. Next, we introduce a novel device resource matching technique based on the proposed model. Compared with previous works, experimental results show that our technique achieves better recall and precision and meets WMSN application needs more effectively. Furthermore, our proposed approach greatly reduces the design complexity as well as the workload of application designers.

1 Introduction

Wireless multimedia sensor networks (WMSNs) [1] have drawn the attention of researchers in recent years, driven by a wealth of theoretical and practical challenges. This growing interest can be largely attributed to new applications enabled by large-scale networks of small devices, such as multimedia video surveillance systems, traffic avoidance and control systems, environment monitoring systems and so on [2]. Most WMSN applications require coordination and cooperation among large-scale multimedia devices in ubiquitous computing environments [3]. The matching between the large-scale device collaboration demand and device resources is the basis of the realization of WMSN applications. The resource ability matching technique [4], which is always a research hotspot in the field of collaboration technology [5–7], is realized by matching the requirement of virtual device capacity with the description of device resource ability.

In the WMSNs’ environment, the multimedia device resource ability has the following characteristics [8]: (a) It is related to the spatial extent [9], i.e., the device resource ability in a certain point of the space depends on both the absolute spatial position of this device resource and the relative spatial distance between this point and itself [10, 11]. (b) It can be superimposed, i.e., superimposing multiple device resource ability in a certain point of the space may achieve better device ability in this point and meet greater requirement which single device could not satisfy [12–14].

Resource matching researches [15–20] currently focus on the web service matching, including syntax service matching [21] and semantic service matching [22]. The former [23] do exact keyword matching by registration information including the name and valid property values of the service in the service library, while the latter is based on semantic web technology. With the combination of ontology repository, matching service and authentication service, the latter can determine the matching degree between the request and service ability [24].

However, both the syntax service matching and the semantic matching are limited. The syntax service matching method [25] cannot accurately describe the desired multimedia device ability and measure the distance between the physical device ability and the desired device ability. Besides, the high user involvement [26] makes it hard to do service matching automatically in ubiquitous computing environment. The semantic service matching method, due to the huge type and number of devices in WMSNs, could not establish the ontology repository and make users understand easily. Furthermore, in the current study [27–29], since resource ability modeling always regards the resource access interface as a function and lacks the spatial location information of device, users build their own collaborative process only by understanding the details of all devices in the system [30], which is not suitable for WMSN applications in the WMSNs.

Given the above problems, this paper proposes a multimedia device resource matching method based on the device resource ability model. The remainder of the paper is organized as follows: Section 2 introduces the related work, Section 3 first defines a multimedia device ability model with spatial location information and its calculation formula which can calculate the device ability accurately and then gives a device resource matching method based on the above model to reduce the complexity in the design of large-scale device collaboration process of WMSN application, Section 4 gives the experimental results, and Section 5 concludes the paper.

2 Related work

Currently, there are various syntax service modeling techniques such as Web Service Description Language (WSDL), Resource Specification Language, Resource Description Framework (RDF), etc. Besides, there are also semantic service modeling techniques which include the RDFS (RDF Schema) [26], Web Ontology Language (OWL)/OWL-S [29], WSDL [27, 28], etc. Among them, the OWL-S has become a very important general resource description technique, which describes the attribute and function of the web resource from the syntax perspective and service perspective. Recently, researchers try to combine the OWL-S technique with the WSDL technique to achieve a new resource description resolution which has comprehensive superiority.

The syntax service matching method [31] focuses on the implementation details of the resource interface, which makes it easy to implement [32]. However, the recall and precision of this method is lower, and it cannot effectively support users doing service discovery based on functional semantic. The typical study is Universal Description, Discovery, and Integration (UDDI) [33–35]. UDDI uses WSDL to describe its web service and does exact keyword matching by the registration information in service library, mainly on ID, name, and valid property values of service.

The semantic service matching technique [36] focuses on the web service matching [37], including single service resource matching and service-oriented resource association matching [38]. The former research [39] used DAML-S for service modeling, which combines the users’ needs with the properties of candidate service to achieve a single downlink (DL) expression. It judged the relationship between two collections of corresponding DL expressions by the DL inference engine and expanded the service matching algorithm. The latter research [40] solved the optimal matching problem among services using an exhaustive method. As the exponential growth of elapsed time, there will come a time-consuming problem when the parameters of service operation become larger [41], giving a service-oriented resource association matching mainly on interface parameter matching which does not filter the service matching according to the corresponding priority [42].

3 Modeling and matching method

3.1 Definition

In this section, we give the formal definition of device and device operation at first and then define the operation ability and performance attribute of device resource.

Definition 1

Terminal. In the large-scale device collaborative system, terminal is the smallest unit of the system control. Each terminal has finite states. Assume t _i is a terminal, its state set is {t s₁,t s₂,…,t s _m }, and one terminal can only be in one state at a time.

Definition 2

Device. In the large-scale device collaborative system, a device is composed by n terminals and denoted as d = {t₁,t₂,…,t _n }. The operation set supported by device d is Σ, and the state set of device is Q.

Definition 3

Device state. The state of device d = {t₁,t₂,…,t _n } is composed of n terminal states. Assume that device d has k states, the state set Q of device d is Q = {s₁,s₂,…,s _k }. Each device state s is an ordered pair of n terminal state, i.e., s = {t s_{t 1},t s_{t 2},…,t s _{t n} }, where t s _{t i} represents a state of terminal t _i .

According to the definition of the device state, we find that if a device contains n terminals and each terminal has m states, the number of device state is mⁿ.

Definition 4

Device operation. Device operation changes the device state by changing some terminal state of this device. Assume the collection Σ of device operation in device d = {t₁,t₂,…,t _n } is Σ = {o p₁,o p₂…,o p _l }, where o p = {<t _i ,t s _i >,…,<t _j ,t s _j >},t _i ,…,t _j ∈{t₁,t₂,…,t _n }.

In the large-scale device collaborative system, according to the impact on the device state, the operating ability of device resource can be divided into three categories: monitor ability, basic control ability, and combination control ability. The first ability of device resource is monitor operation, whose collection is empty, and it is used for gaining the current state of the device. The second one is terminal control operation, whose collection contains only one element, and it can only set one terminal state each time. The last one is advanced control operation, whose collection contains multiple elements, and it can set several terminal states each time.

Definition 5

Composite device. In the large-scale device collaborative system, it is necessary that several devices work in parallel to achieve a synergistic effect, where all these devices are called a composite device.

Assume $d_1=\{t_{11},t_{12},\dots ,t_{1n}\},\dots ,d_k=\{t_{k1},t_{k2},\dots ,t_{\mathit{\text{kn}}}\}$ is a set of concurrent collaborative working devices. Then, its corresponding device operation set is Σ₁,…,Σ _k , and the device state set is Q₁,…,Q _k . The composite device c o m _d contains $\sum _{i=1}^k\mathit{\text{in}}$ terminals, i.e., c o m _d = {d₁,…,d _k }.

The operation set supported by composite device c o m _d is c o m _Σ = {c o m _o p₁,c o m _o p₂,…,c o m _o p _r }, $\mathit{\text{com}}\text{\_}\mathit{\text{op}}=\bigcup _{i=1}^{k}o{p_{\mathit{\text{ij}}}}_i,o{p_{\mathit{\text{ij}}}}_i\in {\Sigma }_i,1\le j_i\le \left|{\Sigma }_i\right|$, and the number of composite device is $r=\prod _{i=1}^k\left|{\Sigma }_i\right|$.

The state set of composite device c o m _d is c o m _q = {c o m _s₁, c o m _s₂,…,c o m _s _s }, $\mathit{\text{com}}\text{\_}s=\sum _{i=1}^k$s _{i j i} ,s _{i j i} ∈Q _i ,1≤j _i ≤|Q _i |,and the number of composite device is $s=\prod _{i=1}^k\left|Q_i\right|$.

3.2 The device resource ability model

In the large-scale device collaborative system, the minimum ability calculation unit is the terminal ability model of the device. The ability of a device’s operation is a union of the ability of terminal controlled by this device. The following is the description and calculation formula of these ability models.

3.2.1 Terminal ability model

Definition 6

Terminal ability. The terminal ability of a device refers to a set of function parameters in its spatial extent and state. Assuming that t is a terminal and its state set is {t s₁,t s₂,…,t s _m }, when t s∈{t s₁,t s₂,…,t s _m }, the terminal ability is as follows:

$$\begin{array}{l}\mathit{\text{tA}}(t,\mathit{\text{ts}})=\mathit{\text{fa}}\left(\sigma \right\{(x_{t1},y_{t1},z_{t1}),\dots ,(x_{\mathit{\text{tm}}},y_{\mathit{\text{tm}}},z_{\mathit{\text{tm}}})\},\\ (a_1,a_2,\dots ,a_n))\mathit{\text{tm}}\ge 1,n\ge 1\end{array}$$

(1)

• s{(x_{t 1},y_{t 1},z_{t 1}),…,(x _{t m} ,y _{t m} ,z _{t m} )} represents the spatial extent of the terminal, and t m = 1 indicates that the ability is on the point (x_{t 1},y_{t 1},z_{t 1}).

• a _i ,1=i=n represents the i th function parameter of the terminal.

• fa represents the map function between function parameters and spatial extent.

For example, denoting the state set in terminal of device d = {t₁,t₂,…,t _n } as {ON,OFF}, when the state of terminal t is ON its ability is t A(t,ON) = l×r(l x){(x₀,y₀),π×((r+Δ r)²−r²),0≤r≤r_max}, and when the state is OFF, its terminal ability is t A(t,OFF) = 0(l x){(x₀,y₀),π×((r+Δ r)²−r²),0≤r≤r_max}. That is to say, in the two-dimensional spatial extent (x₀,y₀),π×((r+Δ r)²−r²), the ability of terminal t is l×r.

3.2.2 Device ability model

Definition 7

Device operation ability. The device operation ability refers to the aggregation of terminal ability in the spatial extent of device operation when this terminal is in its changing state. For device d = {t₁,t₂,…,t _n } and device operation o p = {<t _i ,t s _i >,…,<t _j ,t s _j >}, t _i ,…,t _j ∈{t₁,t₂,…,t _n }, the device operation ability is as follows:

$$\begin{array}{l}\mathit{\text{opA}}\left(\mathit{\text{op}}\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{op}}1},y_{\mathit{\text{op}}1},z_{\mathit{\text{op}}1}),\dots ,(x_{\mathit{\text{opm}}},y_{\mathit{\text{opm}}},z_{\mathit{\text{opm}}})\},\\ (a_1,a_2,\dots ,a_n)),\mathit{\text{opm}}\ge 1,n\ge 1\end{array}$$

(2)

• s{(x_{o p 1},y_{o p 1},z_{o p 1}),…,(x _{o p m} ,y _{o p m} ,z _{o p m} )} represents the spatial extent of the device operation.

• a _i ,1=i=n represents the i th function parameter of the device operation.

• f a represents the map function between function parameters and spatial extent.

Rule 1. Device operation ability calculation. Assume the function Ω is the cumulative calculation function of spatial extent, and Φ _i is the aggregation function of the i th function parameter, Φ = {Σ,MAX,…}. The spatial extent and calculation rule of the function parameter of the device operation is as follows:

• The space extent of the device operation can be calculated by all the space extents of terminal controlled by this device, i.e.,

  $$\begin{array}{l}s\left\{\right(x_{\mathit{\text{op}}1},y_{\mathit{\text{op}}1},z_{\mathit{\text{op}}1}),\dots ,(x_{\mathit{\text{opm}}},y_{\mathit{\text{opm}}},z_{\mathit{\text{opm}}}\left)\right\}\\ =O\left(s\right\{(x_{\mathit{\text{ti}}1},y_{\mathit{\text{ti}}1},z_{\mathit{\text{ti}}1}),\dots ,(x_{\mathit{\text{tim}}},y_{\mathit{\text{tim}}},z_{\mathit{\text{tim}}})\},\dots ,\\ s\left\{\right(x_{\mathit{\text{tj}}1},y_{\mathit{\text{tj}}1},z_{\mathit{\text{tj}}1}),\dots ,(x_{\mathit{\text{tjm}}},y_{\mathit{\text{tjm}}},z_{\mathit{\text{tjm}}}\left)\right\}).\end{array}$$

  (3)

• The function parameter of the device operation in spatial extent s{(x₁,y₁,z₁),…,(x _m ,y _m ,z _m )} can be aggregated by all these function parameters of terminal controlled by this device in the same spatial extent, i.e.,

  $$\begin{array}{l}s\left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_i\\ =F_i\left(s\right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:a_{\mathit{\text{ii}}},\dots ,\\ s\left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{ji}}}),\end{array}$$

  (4)

  where a _{i i} ,…,a _{j i} is the corresponding function parameter of terminal control ability by the device operation.

For example, denoting the operation of device d = {t₁,…,t _n } as o p = {<t₁,ON>,<t₂,ON>}, when the state of terminal t₁ is ON, its terminal ability is $\mathit{\text{tA}}(t_1,\text{ON})=l_1\times r_1\left(\mathit{\text{lx}}\right)\left\{\right(x_{01},y_{01}),\pi \times ({(r_1+\mathrm{\Delta r})}^2-{r_1}^2),0\le r_1\le r_{max}^1\}$, and when the state of terminal is OFF, its terminal ability is $\mathit{\text{tA}}(t_2,\text{OFF})=l_2\times r_2\left(\mathit{\text{lx}}\right)\left\{\right(x_{02},y_{02}),\pi \times ({(r_2+\mathrm{\Delta r})}^2-{r_2}^2),0\le r_2\le r_{max}^2\}$. That is to say, in the two-dimensional spatial extent (x₀,y₀),π×((r+Δ r)²−r²), the ability of terminal t is l×r.

Assume the operation space intersection for terminal t₁ and t₂ is as follows:

$$\begin{array}{l}\left\{\right(x_{01},y_{01}),\pi \times ({(r_1+\mathrm{\Delta r})}^2-{r_1}^2),0\le r_1\le r_{max}^1\}\cap \\ \left\{\right(x_{02},y_{02}),\pi \times ({(r_2+\mathrm{\Delta r})}^2-{r_2}^2),0\le r_2\le r_{max}^2\}=\sigma (r_1,r_2).\end{array}$$

(5)

Then, the ability of device operation is as follows:

$$\begin{array}{l}\mathit{\text{opA}}\left(\mathit{\text{op}}\right)=\left\{\begin{array}{l}{l}_1\times r_1\left(\mathit{\text{lx}}\right),\left\{\right(x_{01},y_{01}),\pi \times ({(r_1+\mathrm{\Delta r})}^2-{r_1}^2)-\sigma (r_1,r_2),0<r_1\le r_{max}^1\}\\ \begin{array}{l}(l_1\times r_1+l_2\times r_2)\left(\mathit{\text{lx}}\right),\left\{\sigma \right(r_1,r_2),0\le r_1\le r_{max}^1,0<r_2\le r_{max}^2\}\\ l_2\times r_2\left(\mathit{\text{lx}}\right),\left\{\right(x_{02},y_{02}),\pi \times ({(r_2+\mathrm{\Delta r})}^2-{r_2}^2)-\sigma (r_1,r_2),0<r_2\le r_{max}^2\}.\end{array}\end{array}\right.\end{array}$$

(6)

The spatial extent of device operation OP is the union of the operation spatial extent for terminal t₁ and t₂, i.e., {(x₀₁,y₀₁),π×((r₁+Δ r₁)²−r₁²)}∪{(x₀₂,y₀₂),π×((r₂+Δ r₂)²−r₂²)}. The device operation ability is a piecewise function: in the intersection of two terminal’s operation space σ(r₁,r₂), the ability is (l₁×r₁+l₂×r₂), i.e., the joint result of two terminals; in the terminal t₁’s operation space, the ability is l₁×r₁, i.e., the result of terminal t₁; and in the terminal t₂’s operation space, the ability is l₂×r₂, i.e., the result of terminal t₂.

Definition 8

Device ability. The device ability refers to all the device operation abilities in spatial extent. Assume the collection of device operation in device d = {t₁,t₂,…,t _n } is Σ = {o p₁,o p₂,…,o p _l }. The device ability is denoted as follows:

$$\begin{array}{l}\mathit{\text{dA}}\left(d\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{d1},y_{d1},z_{d1}),\dots ,(x_{\mathit{\text{dm}}},y_{\mathit{\text{dm}}},z_{\mathit{\text{dm}}})\},\\ (a_1,a_2,\dots ,a_n)),\mathit{\text{dm}}\ge 1,n\ge 1\end{array}$$

(7)

• σ{(x_{d 1},y_{d 1},z_{d 1}),…,(x _{d m} ,y _{d m} ,z _{d m} )} denotes the spatial extent of the device.

• a _i ,1≤i≤n denotes the i th function parameter of the device.

• f a denotes the map function between function parameters and spatial extent.

Rule 2. Device ability calculation. Assume the function Ω is the cumulative calculation function of spatial extent, Φ _i is the aggregation function of the i th function parameter, Φ = {Σ,MAX,…}. The spatial extent and calculation rule of function parameter of a device is as follows:

• The space extent of a device can be calculated by all the space extents of the terminals controlled by this device, i.e.,

  $$\begin{array}{l}\begin{array}{l}\sigma \left\{\right(x_{d1},y_{d1},z_{d1}),\dots ,(x_{\mathit{\text{dm}}},y_{\mathit{\text{dm}}},z_{\mathit{\text{dm}}}\left)\right\}\\ =\Omega \left(\sigma \right\{(x_{t11},y_{t11},z_{t11}),\dots ,(x_{t1m},y_{t1m},z_{t1m})\},\dots ,\\ \sigma \left\{\right(x_{\mathit{\text{tn}}1},y_{\mathit{\text{tn}}1},z_{\mathit{\text{tn}}1}),\dots ,(x_{\mathit{\text{tnm}}},y_{\mathit{\text{tnm}}},z_{\mathit{\text{tnm}}}\left)\right\}).\end{array}\end{array}$$

  (8)

• The function parameter of a device in spatial extent σ{(x₁,y₁,z₁),…,(x _m ,y _m ,z _m )} can be aggregated by all the device operations in the same spatial extent. The minimum value of device function parameter a _i in this spatial extent is:

  $$\begin{array}{l}\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{\mathit{\text{ai}}}\\ =\text{MIN}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:a_{1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{li}}}),\end{array}\end{array}$$

  (9)

  where a_1i,…,a _{l i} is the corresponding function parameter of device ability.

The maximum value of device function parameter a _i in this spatial extent is:

$$\begin{array}{l}\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{ai}}}\\ =\text{MAX}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:a_{1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{li}}}),\end{array}\end{array}$$

(10)

where the interval of function parameter a _i is:

$$\begin{array}{l}\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_i\\ =\left[\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:{\theta }_{min}^{\mathit{\text{ai}}},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{ai}}}]\\ =\left[\text{MIN}\right(\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{li}}}),\\ \text{MAX}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:a_{1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{li}}}\left)\right]\end{array}\end{array}$$

(11)

For example, assume the operation set of device d = {t₁,t₂,t₃} is Σ = {o p₁,o p₂}, o p₁ = {<t₁,ON>,<t₂,ON>}, o p₂ = {<t₂,OFF>,<t₃,ON>}. The abilities of device operation o p₁ and o p₂ are:

$$\begin{array}{l}\mathit{\text{opA}}\left(o{p}_1\right)=\left\{\begin{array}{l}{l}_1\times r_1\left(\mathit{\text{lx}}\right),\left\{\right(x_{01},y_{01}),\pi \times ({(r_1+\mathrm{\Delta r})}^2-{r_1}^2)-\sigma (r_1,r_2),0<r_1\le r_{max}^1\}\\ \begin{array}{l}(l_1\times r_1+l_2\times r_2)\left(\mathit{\text{lx}}\right),\left\{\sigma \right(r_1,r_2),0\le r_1\le r_{max}^1,0<r_2\le r_{max}^2\}\\ l_2\times r_2\left(\mathit{\text{lx}}\right),\left\{\right(x_{02},y_{02}),\pi \times ({(r_2+\mathrm{\Delta r})}^2-{r_2}^2)-\sigma (r_1,r_2),0<r_2\le r_{max}^2\}\end{array}\end{array}\right.\end{array}$$

(12)

$$\begin{array}{l}\mathit{\text{opA}}\left(o{p}_2\right)=l_3\times r_3\left(\mathit{\text{lx}}\right)\pi \\ \times ({(r_3+\mathrm{\Delta r})}^2-{r_3}^2),0\le r_3\le r_{max}^3\end{array}$$

(13)

The intersection of operation spatial extent of terminals t₁, t₂, and t₃ is:

$$\begin{array}{l}\left\{\right(x_{01},y_{01}),\pi \times ({(r_1+\mathrm{\Delta r})}^2-{r_1}^2),0\le r_1\le r_{max}^1\}\cap \\ \left\{\right(x_{02},y_{02}),\pi \times ({(r_2+\mathrm{\Delta r})}^2-{r_2}^2),0\le r_2\le r_{max}^2\}\cap \\ \left\{\right(x_{03},y_{03}),\pi \times ({(r_3+\mathrm{\Delta r})}^2-{r_3}^2),0\le r_3\le r_{max}^3\}=\sigma (r_1,r_2,r_3).\end{array}$$

(14)

Assume l₁≤l₂≤l₃, then the minimum device ability in operation spatial extent σ(r₁,r₂,r₃) is:

$$\begin{array}{l}{\theta }_{min}^a=\text{MIN}\left(\right(l_1+l_2)\times r(\mathit{\text{lx}}),l_3\times r(\mathit{\text{lx}}\left)\right)=l_3\times r\left(\mathit{\text{lx}}\right).\end{array}$$

(15)

The maximum device ability is:

$${\theta }_{max}^a=\text{MAX}\left(\right(l_1+l_2)\times r(\mathit{\text{lx}}),l_3\times r(\mathit{\text{lx}}\left)\right)=(l_1+l_2)\times r\left(\mathit{\text{lx}}\right).$$

(16)

The function parameter interval of the device ability is:

$$\begin{array}{l}a=[{\theta }_{min}^a,{\theta }_{max}^a]=[l_3\times r(\mathit{\text{lx}}),(l_1+l_2)\times r(\mathit{\text{lx}}\left)\right].\end{array}$$

(17)

Similarly, when the operation spatial extent is $r_{max}^1<r\le r_{max}^2$ or $r_{max}^2<r\le r_{max}^3$, the corresponding function parameter interval of the device ability is:

$$a=[{\theta }_{min}^a,{\theta }_{max}^a]=[l_2\times r(\mathit{\text{lx}}),l_3\times r(\mathit{\text{lx}}\left)\right].$$

(18)

$$a=[{\theta }_{min}^a,{\theta }_{max}^a]=[l_3\times r(\mathit{\text{lx}}),l_3\times r(\mathit{\text{lx}}\left)\right].$$

(19)

The ability of the device d = {t₁,t₂,t₃} is:

$$\begin{array}{l}\mathit{\text{dA}}\left(d\right)=\left\{\begin{array}{l}[l_3\times r(\mathit{\text{lx}}),(l_1+l_2)\times r(\mathit{\text{lx}}\left)\right]\pi \times ({(r+\mathrm{\Delta r})}^2-r^2),0\le r\le r_{max}^1\\ \left[\right(l_2\times r\left(\mathit{\text{lx}}\right),l_3\times r\left(\mathit{\text{lx}}\right)]\pi \times ({(r+\mathrm{\Delta r})}^2-r^2),r_{max}^1<r\le r_{max}^2\\ [l_3\times r(\mathit{\text{lx}}),[l_3\times r\left(\mathit{\text{lx}}\right)]\pi \times ({(r+\mathrm{\Delta r})}^2-r^2),r_{max}^2<r\le r_{max}^3\end{array}\right.\end{array}$$

(20)

3.2.3 Composite device ability model

Definition 9

Composite device operation ability. It refers to the aggregation of device operation ability when devices execute concurrently in the spatial extent of device operation. Assume c o m D = {d₁,…,d _k } is a composite device; the operation of this device is $\mathit{\text{comOp}}=\bigcup _{i=1}^{k}o{p}_i,o{p}_i\in {\Sigma }_i$, and the operation ability is as follows:

$$\begin{array}{l}\mathit{\text{comOpA}}\left(\mathit{\text{comOp}}\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{comOp}}1},y_{\mathit{\text{comOp}}1},z_{\mathit{\text{comOp}}1}),\dots ,\\ (x_{\mathit{\text{comOpm}}},y_{\mathit{\text{comOpm}}},z_{\mathit{\text{comOpm}}})\},\\ (a_1,a_2,\dots ,a_n)),\mathit{\text{comOpm}}\ge 1,n\ge 1\end{array}$$

(21)

• σ{(x_{c o m O p 1},y_{c o m O p 1},z_{c o m O p 1}),…,(x _{c o m O p m} , y _{c o m O p m} ,z _{c o m O p m} )} represents the spatial extent of the device operation.

• a _i ,1≤i≤n represents the i th function parameter of the device operation.

• f a represents the map function between function parameters and spatial extent.

Rule 3. Composite device operation ability calculation. Assume the function Ω is the cumulative calculation function of spatial extent and is the aggregation function of the i th function parameter, Φ = {Σ,MAX,…}. Then, the spatial extent and calculation rule of function parameter of composite device operation is as follows:

• The space extent of composite device operation can be calculated by all the space extents of participated device operations, i.e.,

  $$\begin{array}{l}\sigma \left\{\right(x_{\mathit{\text{comOp}}1},y_{\mathit{\text{comOp}}1},z_{\mathit{\text{comOp}}1}),\dots ,(x_{\mathit{\text{comOpm}}},y_{\mathit{\text{comOpm}}},z_{\mathit{\text{comOpm}}}\left)\right\}\\ =\Omega \left(\sigma \right\{(x_{\mathit{\text{op}}11},y_{\mathit{\text{op}}11},z_{\mathit{\text{op}}11}),\dots ,(x_{\mathit{\text{op}}1m},y_{\mathit{\text{op}}1m},z_{\mathit{\text{op}}1m})\},\\ ,\dots \sigma \left\{\right(x_{\mathit{\text{opk}}1},y_{\mathit{\text{opk}}1},z_{\mathit{\text{opk}}1}),\dots ,(x_{\mathit{\text{opkm}}},y_{\mathit{\text{opkm}}},z_{\mathit{\text{opkm}}}\left)\right\})\end{array}$$

  (22)

• The function parameter of composite device operation in spatial extent σ{(x₁, y₁, z₁), …,(x _m ,y _m , z _m )} can be aggregated by all these function parameters of participated device operations in the same spatial extent, i.e.,

  $$\begin{array}{l}\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_i\\ ={\Phi }_i\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:a_{1i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_{\mathit{\text{ki}}}),\end{array}\end{array}$$

  (23)

  where a_1i,…,a _{l i} is the corresponding function parameter of the device operation ability.

Definition 10

Composite device ability. The composite device ability refers to all the participated device abilities in spatial extent. Assume c o m D = {d₁,…,d _k } is a composite device. The ability is denoted as follows:

$$\begin{array}{l}\mathit{\text{comDA}}\left(\mathit{\text{comD}}\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{comD}}1},y_{\mathit{\text{comD}}1},z_{\mathit{\text{comD}}1}),\dots ,\\ (x_{\mathit{\text{comDm}}},y_{\mathit{\text{comDm}}},z_{\mathit{\text{comDm}}})\},\\ (a_1,a_2,\dots ,a_n)),\mathit{\text{comDm}}\ge 1,n\ge 1\end{array}$$

(24)

• σ{(x_{c d s 1},y_{c d s 1},z_{c d s 1}),…,(x _{c d s m} ,y _{c d s m} ,z _{c d s m} )} represents the spatial extent of composite device.

• a _i ,1≤i≤n represents the i th function parameter of the device.

• f a represents the map function between function parameters and spatial extent.

Rule 4. Composite device ability calculation. Assume the function Ω is the cumulative calculation function of spatial extent, Φ _i is the aggregation function of the i th function parameter, and Φ = {Σ,MAX,…}. The spatial extent and calculation rule of function parameter of composite device is as follows:

• The space extent of composite device can be calculated by all the space extents of participated devices, i.e.,

  $$\begin{array}{l}\sigma \left\{\right(x_{\mathit{\text{cds}}1},y_{\mathit{\text{cds}}1},z_{\mathit{\text{cds}}1}),\dots ,(x_{\mathit{\text{cdsm}}},y_{\mathit{\text{cdsm}}},z_{\mathit{\text{cdsm}}}\left)\right\}\\ =\Omega \left(\sigma \right\{(x_{d11},y_{d11},z_{d11}),\dots ,(x_{d1m},y_{d1m},z_{d1m})\},\dots ,\\ \sigma \left\{\right(x_{\mathit{\text{dk}}1},y_{\mathit{\text{dk}}1},z_{\mathit{\text{dk}}1}),\dots ,(x_{\mathit{\text{dkm}}},y_{\mathit{\text{dkm}}},z_{\mathit{\text{dkm}}}\left)\right\})\end{array}$$

  (25)

• The function parameter of composite device in spatial extent σ{(x₁,y₁,z₁),…,(x _m ,y _m ,z _m )} can be aggregated by all these function parameters of participated devices in the same spatial extent, i.e., the minimum value of the device function parameter in this spatial extent is:

  $$\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{\mathit{\text{ai}}}\\ =\text{MIN}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:{\theta }_{min}^{a1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{a2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{\mathit{\text{aki}}}),\end{array}$$

  (26)

  where a_1i,…,a _{l i} is the corresponding function parameter of composite device ability.

The maximum value of device function parameter a _i in this spatial extent is:

$$\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{ai}}}\\ =\text{MAX}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:{\theta }_{max}^{a1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{a2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{aki}}}),\end{array}$$

(27)

where the interval of the function parameter a _i is:

$$\begin{array}{l}\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_i\\ =\left[\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:{\theta }_{min}^{\mathit{\text{ai}}},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{ai}}}]\\ =\left[\text{MIN}\right(\sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{a1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{a2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{min}^{\mathit{\text{aki}}}),\\ \text{MAX}\left(\sigma \right\{(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m)\}:{\theta }_{max}^{a1i},\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{a2i},\dots ,\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:{\theta }_{max}^{\mathit{\text{aki}}}\left)\right].\end{array}$$

(28)

3.3 Device resource matching method based on ability model

Resource matching is a process [43, 44] which selects a resource candidate set among the device resource sets based on a given user demand. Every device resource contained in the resource candidate set meets the demand. We first give the description of user device ability requirements as follows and then propose the device ability matching method in a large-scale device collaboration system.

3.3.1 Description of user demand

The user demand for device resource ability [45, 46] involved in a process is a sum of all ability demands for virtual device resource. It is denoted as R = {r₁,…,r _m },m≥1, where r presents a description of a user’s demand, i.e., the ability demand of virtual device resource. r can be presented in a tuple: r = (dss,rs).

• dss = ds represents the virtual device resource involved in the description of user demand.

• rs represents the user demand for the virtual device resource ability in the description: rs = dA(ds₁).

3.3.2 Device ability matching method

Rule 5. Device ability matching. Assume R = {r₁,…,r _m },m≥1 is the description of user demand in device collaboration process. The demand of device resource ability r = (dss,rs),rs = dA(ds) is as follows:

$$\begin{array}{l}\mathit{\text{dA}}\left(\mathit{\text{ds}}\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{ds}}1},y_{\mathit{\text{ds}}1},z_{\mathit{\text{ds}}1}),\dots ,(x_{\mathit{\text{dsm}}},y_{\mathit{\text{dsm}}},z_{\mathit{\text{dsm}}})\},\\ (a_{1s},a_{2s},\dots ,a_{\mathit{\text{ns}}})),\mathit{\text{dsm}}\ge 1,\mathit{\text{ns}}\ge 1,\end{array}$$

(29)

and the ability of device d is:

$$\begin{array}{l}\mathit{\text{dA}}\left(d\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{d1},y_{d1},z_{d1}),\dots ,(x_{\mathit{\text{dm}}},y_{\mathit{\text{dm}}},z_{\mathit{\text{dm}}})\},\\ (a_1,a_2,\dots ,a_n)),\mathit{\text{dm}}\ge 1,n\ge 1.\end{array}$$

(30)

If device d meets the following three conditions at the same time, then we believe that this device matches the device resource ability in users’ demand:

• $$\begin{array}{l}\sigma \left\{\right(x_{\mathit{\text{ds}}1},y_{\mathit{\text{ds}}1},z_{\mathit{\text{ds}}1}),\dots ,(x_{\mathit{\text{dsm}}},y_{\mathit{\text{dsm}}},z_{\mathit{\text{dsm}}})\\ \underline{\prec }\sigma \left\{\right(x_{d1},y_{d1},z_{d1}),\dots ,(x_{\mathit{\text{dm}}},y_{\mathit{\text{dm}}},z_{\mathit{\text{dm}}})\end{array}$$

  (31)
• $$(a_{1s},a_{2s},\dots ,a_{\mathit{\text{ns}}})\subseteq (a_1,a_2,\dots ,a_n)$$

  (32)
• $$\begin{array}{l}\text{If}\forall \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}\\ \underline{\prec }\sigma \left\{\right(x_{\mathit{\text{ds}}1},y_{\mathit{\text{ds}}1},z_{\mathit{\text{ds}}1}),\dots ,(x_{\mathit{\text{dsm}}},y_{\mathit{\text{dsm}}},z_{\mathit{\text{dsm}}}),\\ \text{then}\forall a_{\mathit{\text{is}}}\in (a_{1s},a_{2s},\dots ,a_{\mathit{\text{ns}}})\\ \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:\\ a_{\mathit{\text{is}}}\cap \sigma \left\{\right(x_1,y_1,z_1),\dots ,(x_m,y_m,z_m\left)\right\}:a_i\ne \phi \\ \text{where}{a}_{\mathit{\text{is}}}\text{and}{a}_i\text{are}\text{the}\text{same}\text{function}\text{parameter}\\ a_i\in (a_1,a_2,\dots ,a_n).\end{array}$$

  (33)

3.3.3 Composite device ability matching method

Definition 11

Matching distance of device ability. It refers to the size of operation spatial extent and function parameter matching between two devices. Assume the abilities of devices d₁ and d₂ are as follows:

$$\begin{array}{l}\mathit{\text{dA}}\left(d_1\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{d11},y_{d11},z_{d11}),\dots ,(x_{d1m},y_{d1m},z_{d1m})\},\\ (a_{11},a_{12},\dots ,a_{1n})),d1m\ge 1,1n\ge 1\end{array}$$

(34)

$$\begin{array}{l}\mathit{\text{dA}}\left(d_2\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{d21},y_{d21},z_{d21}),\dots ,(x_{d2m},y_{d2m},z_{d2m})\},\\ (a_{21},a_{22},\dots ,a_{2n})),d2m\ge 1,2n\ge 1.\end{array}$$

(35)

The matching distance of device ability is calculated as follows:

$$F\left(\mathit{\text{dA}}\right(d_1),\mathit{\text{dA}}(d_2\left)\right)=\sqrt{{({\sigma }_1-{\sigma }_2)}^2+\sum _{i=1}^n{(a_{1i}-a_{2i})}^2}$$

(36)

• $$\begin{array}{l}{\sigma }_1=\sigma \left\{\right(x_{11},y_{11},z_{11}),\dots ,(x_{1m},y_{1m},z_{1m}\left)\right\},\\ {\sigma }_2=\sigma \left\{\right(x_{21},y_{21},z_{21}),\dots ,(x_{2m},y_{2m},z_{2m}\left)\right\}\end{array}$$

  (37)

• a_1i and a_2i are the same function parameters, a_1i∈(a₁₁,a₁₂,…,a_1n),a_2i∈(a₂₁,a₂₂,…,a_2n).

Now, we give our composite ability matching algorithm based on the above matching distance. Assume D _s ={d₁,…,d _l },l≥0 is the device satisfied by users, and D _{a l} ={d₁,…,d _k },k≥1 is the device in the large-scale collaborative system. The description of algorithm is as follows:

• 1. The first input is the device resource ability r = (d s s,r s),r s = d A(d s) of users’ demand R = {r ₁,…,r _m },m≥1 in the large-scale collaborative system, i.e.,

  $$\begin{array}{l}\mathit{\text{dA}}\left(\mathit{\text{ds}}\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{ds}}1},y_{\mathit{\text{ds}}1},z_{\mathit{\text{ds}}1}),\dots ,(x_{\mathit{\text{dsm}}},y_{\mathit{\text{dsm}}},z_{\mathit{\text{dsm}}})\},\\ (a_{1s},a_{2s},\dots ,a_{\mathit{\text{ns}}})),\mathit{\text{dsm}}\ge 1,\mathit{\text{ns}}\ge 1.\end{array}$$

  (38)

  The second input is the device set D = D _{a s} −D _s = {d₁,…,d _n },n≥1 except that meets the demand, i.e.,

  $$\begin{array}{l}\mathit{\text{dA}}\left(d_i\right)=\mathit{\text{fa}}\left(\sigma \right\{(x_{\mathit{\text{di}}1},y_{\mathit{\text{di}}1},z_{\mathit{\text{di}}1}),\dots ,(x_{dim},y_{dim},z_{dim})\},\\ (a_{1i},a_{2i},\dots ,a_{\mathit{\text{ni}}})),dim\ge 1,\mathit{\text{ni}}\ge 1.\end{array}$$

  (39)

• 2. Set the matching depth of composite device be k.

• 3. Calculate the device ability matching distance F(d A(d _i ),d A(d s)),d _i ∈D in device set D = {d ₁,…,d _n },n≥1.

• 4. Add the device d _i with the minimum matching distance to the candidate composite device c o m D _k where D = D−d _i , d A(d s) = d A(d s)−d A(d _i ).

• 5. Repeat steps 3 and 4 until d A(d s) = 0; now c o m D _k is a candidate composite device.

• 6. If all the devices are traversed and d A(d s)≠0, the algorithm terminates; otherwise, repeat steps 3, 4, and 5 to find the candidate composite devices c o m D _k which meet the matching depth k.

The following is the detail algorithm (Algorithm 1):

Algorithm 1 Composite device ability matching method

4 Experiments

4.1 Configuration

The experiments are conducted in three desktop computers: the first one simulates the requests of device collaborative tasks, the second one deploys a large-scale device collaboration system which uses our proposed matching method to assign the device resource with collaborative tasks, and the last one deploys a device simulator which can produce a lot of device resources with different abilities. Tables 1 and 2 show the configuration and parameters in our experiments, respectively.

Table 1 Configuration of ability matching experiment

Table 2 Parameters of ability matching experiment

4.2 Effect of matching method

This part measures the relationship between the number of candidate device resource in virtual device and the total number of device resource in a system. Assume the matching depth of device ability depth=40, the parameter number of device ability AN=2, and the number of device in large-scale collaborative system is 1,000, 2,000,…, 20,000, respectively. In order to reduce the random error, each test performs 50 times.

Figure 1 and 2 show the result: When the total number of devices D N = 1,000, our matching method can ensure the percentage of candidate device resource to be more than 75; and with the growth of the total number, this percentage becomes nearly 100, which indicates that our matching method can meet the effect demand of large-scale device collaboration system.

Figure 1

Distribution of candidate device resource ( depth = 20 ).

Figure 2

Distribution of candidate device resource ( depth = 40 ).

4.3 Performance of matching method

This part tests the performance of our matching method. Assume the matching depth of device ability depth = 40, the parameter number of device ability AN = 2, and the number of device in large-scale collaborative system be 1,000, 2,000, …, 20,000, respectively. In order to reduce the random error, each test is performed 50 times.

Figure 3 and 4 show the result due to the pre-processing operation for device matching; the growth of the total number of device in the system only affects the increase in time of pre-processing, while it affects matching time a little. Besides, when DN = 20,000, the total time of device ability matching is less than 6 s, which indicates that our matching method can meet the performance demand of large-scale device collaboration system. The details are in Tables 3 and 4.

Figure 3

Time of device ability matching ( depth = 20 ).

Figure 4

Time of device ability matching ( depth = 40 ).

Table 3 The number of matching resources in the case of different devices

Table 4 The time of matching resources in the case of different devices

4.4 Matching method between single and composite device

This part measures the relationship between matching time and number of device resource in the case of different matching depths. Assume the matching depth of device ability depth = 20,40,60,80,and 100, respectively, the parameter number of device ability AN = 2, and the number of device in large-scale collaborative system be 1,000,2,000,…,20,000, respectively. In order to reduce the random error, each test is performed 50 times.

Figure 5 and 6 show the result when depth<40; the matching time of both single and composite device increase slowly with the growth of total device number DN. The reason is that in device ability matching process, we should use as less as possible devices to meet the demand of virtual device in order to reduce the conflict of device operations. Thus, when the matching depth is small, single device will meet the requirement.

Figure 5

The relationship between single device matching time and DN.

Figure 6

The relationship between composite device matching time and DN.

However, the matching time of composite device grows rapidly with the growth of total device number DN, which indicates that considering both the less conflict of device operation and higher matching efficiency of device ability, we should select as less as possible devices to meet the demand of virtual device.

4.5 Comparisons with traditional methods

At present, resources matching research mainly conclude syntax service matching and semantic service matching. In order to compare the efficiency of our proposed device resource matching method based on the device resource ability model with the two traditional resource matching methods, we designed a set of comparative experiments, testing in the case of the same user requirements how much time is cost by each of the resource matching technique and how many devices are matched up by each of the technique.

Assume the matching depth of device ability depth = 40, the number of device in large-scale collaborative system is 1,000,2,000,…,20,000, respectively. In order to reduce the random error, each test is performed 50 times.

In Figure 7 and 8, horizontal axes show the total number of devices DN in large-scale device collaboration system, and the vertical axes show the cost time used for matching and the number of the candidate devices matched, respectively. It can be seen that syntax service matching method costs the least amount of time, but the matching result is not so good as the result of our proposed method. The number of the candidate devices is far less than the number of matched devices in the case of the device resource ability model matching method and semantic matching method. The effect of the semantic service matching method is almost the same with our proposed device resource matching method based on the device resource ability model, but the semantic service matching method costs much more time than that of our proposed method cost. In order to analyze the experimental results quantitatively, we give part of the experimental data in our experiments (Table 5).

Figure 7

The relationship between matching time and DN in the case of three methods.

Figure 8

The relationship between device number matched up and DN in the case of three methods.

Table 5 Part of the experimental data

5 Conclusions

WMSN applications usually involve a large number of devices and complex spatial information configurations. To address this challenge, in this article, we propose a multimedia device ability model, which incorporates spatial information management into the modeling phase. Based on this model, we further derive a multimedia device resource matching technique for ubiquitous computing environments. By adopting this technology, automatic execution of the collaboration process is done efficiently. Experimental results show that our proposed technique achieves better recall and precision. Compared with previous works, our approach not only has better performance but also meets WMSN application needs much better. The multimedia device ability matching method can greatly reduce the design complexity and workload of application designers.