Federated Learning (FL) has recently garnered considerable attention because it allows multiple clients to collaboratively train machine learning models while keeping their local data private. However, most existing studies focus primarily on optimizing a single FL task, overlooking the dynamic nature of systems where tasks may arrive over time. To address this issue, this paper formulates a novel long-term multi-task FL optimization problem, aiming at balancing the learning quality and the penalties incurred from insufficient client participation in each communication round. To mitigate selection bias, a fairness queue is implemented, and a Lyapunov optimization model is developed to enhance both system stability and learning utility. Furthermore, we derive an upper bound for the objective function, reframing the client selection issue as a minimum weight bipartite matching problem within an auxiliary bipartite graph. The regret of the proposed strategy is theoretically analyzed to quantify the performance gap. Finally, extensive simulations on two real datasets demonstrate the effectiveness of the proposed scheme, highlighting its potential for improving both fairness and efficiency in dynamic, multi-task FL environments.

Federated Learning (FL) mitigates data leakage by sharing only local machine learning models instead of raw data. However, it remains vulnerable to differential attacks. Differential Privacy (DP) addresses this concern by introducing noise to make it challenging for adversaries to reconstruct training samples. Nonetheless, clients often have varying attitudes toward data privacy, quantified by their privacy budgets. Low privacy budgets indicate the stringent privacy requirements of clients, requiring high compensations to incentivize their participation. Focusing solely on privacy budgets, however, can introduce selection bias, potentially compromising model generalization. Therefore, it is essential to emphasizes the fairness of client participation, ensuring that clients with lower privacy budgets also have opportunities to contribute to the training process. To tackle the above challenges, this paper formulates a novel DP-based incentive problem in FL, aiming to optimize the utilities of both the server and the clients. Specifically, we propose an auction mechanism that jointly selects participants based on their heterogeneous privacy budgets and determines appropriate payments. The proposed auction mechanism is proven to achieve several desirable properties, including computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed optimization performance. Finally, simulation results validate the effectiveness of the proposed mechanism.

Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.

Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users' privacy. The existence of multiple concurrent tasks is common in urban environments, so privacy-preserving multi-task data aggregation is essential and useful to a large-scale crowdsensing server. However, existing privacy-preserving data aggregation schemes in MCS mainly focus on the single-task data aggregation and the privacy protection of user's data. Little attention is paid to the privacy of user's decision of accepting tasks. Therefore, we propose a privacy-preserving and server-oriented efficient multi-task data aggregation scheme for MCS based fog computing. The proposed scheme can aggregate multiple concurrent tasks from multiple requesters (e.g., for 9 tasks, the proposed scheme completes all tasks in one round as opposed to existing schemes, which finish 9 tasks in nine rounds). Our scheme protects the privacy of user's decision, user's data, and aggregation result of each requester under collusion attacks. Through formal security analyses, our scheme is proved to be secure and privacy-preserving. Both theoretical analyses and experiments show our scheme is efficient.

This paper investigates the implementation of Federated Learning (FL) in an over-the-air computation system with volatile clients, where each client operates under a limited energy budget and may unexpectedly drop out during local training sessions. The dropout of clients not only wastes energy but also diminishes their participation frequency, necessitating careful client selection by the server in each communication round. However, the diversity of training tasks and the random nature of client dropout present challenges such as the absence of an explicit objective function and the unavailability of client performance metrics. To address these challenges, we first analyze the convergence of the over-the-air federated learning system with volatile clients to identify the key factor influencing the model's convergence speed. Building upon this analysis, we propose an approximation of the objective function as the optimization goal for client selection. To mitigate energy waste, we introduce a dynamic client selection strategy termed DCSE, based on Exp3 with multiple plays and energy constraints, aiming to reconcile the dilemma of unknown local training states and limited resource constraints. Theoretical analysis demonstrates that our proposed solution maintains a constant bound on the difference from the optimal solution, affirming its theoretical feasibility. Furthermore, experimental results validate the effectiveness of the proposed strategy in enhancing FL by accelerating convergence speed, improving test accuracy, and reducing wasted energy.

Federated learning (FL) is a distributed machine learning paradigm that can be organized in two layers. In the outer layer of users, there is a model interaction process between the task publisher and users, through which all parties obtain their respective utilities. However, these parties’ utilities are coupled, both depending on the training sample size and local iterations. In the inner layer of users, a user's multiple devices (e.g., computers and smart phones) can be used to jointly train local models efficiently. Yet, due to device heterogeneity, it is challenging for users to determine which devices to participate in local training and allocate how many computing and communication resources to minimize training costs. In this paper, we tackle this novel two-layer optimization problem by designing utility game and resource control strategies. In the outer layer, we model the relationship between the task publisher and users as a Stackelberg game and obtain the optimal solution for both parties by solving a unique Stackelberg equilibrium point; while in the inner layer, we formulate the optimization problem as a mixed integer nonlinear programming problem, which is decomposed into sub-problems and solved by devising resource control algorithm based on successive convex approximation. Finally, extensive experiments show that the proposed algorithms outperform baseline algorithms.

Mobile edge computing (MEC)deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study themultimodal optimization problem of edge server placement (MESP)with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

Mobile crowdsensing (MCS) has recently received considerable attention due to its capability of providing a promising paradigm to complete complex sensing tasks. Existing works on MCS mainly focus on designing incentive mechanisms to attract mobile users to participate in crowdsensing, while ignoring the freshness of information, i.e.,Age of Information(AoI). Although multiple source nodes with common observation can indeed improve the data quality of MCS, it complicates the calculation of the AoI. To address this issue, this article proposes a freshness-aware incentive mechanism in MCS, which not only captures the conflict interests/competitions among users, but also considers the age of information (AoI). Specifically, we define two data sampling models, namedsampling-at-willmodel andsampling-predeterminedmodel. For both models, we design efficient auction mechanisms, which recruit appropriate mobile users, determine the payments, and schedule the data sampling, so as to optimize the average AoI and data quality under budget constraint. It is proved that the proposed auction achieves several desirable properties, including individual rationality, budget balance, truthfulness and computational efficiency. We also theoretically derive the upper bound of the average AoI obtained by the proposed scheme. Finally, we conduct simulations to evaluate the efficiency of the proposed mechanism in optimizing the data quality and AoI.

To overcome the challenge of limited bandwidth, client selection has been considered an effective method for optimizing Federated Learning (FL). However, since the volatility of the learning environment, the available clients exhibit some volatility over the training process in terms of client population, client data, training status, and transmitting status, which greatly increases the difficulty of client selection. To find a practical solution, we explore a client selection problem in volatile federated learning (Volatile FL). Specifically, we first derive the convergence analysis for non-convex and strongly convex cases to illustrate the main factors affecting the convergence speed. Then, we introduce the client utility to quantify the client’s contribution to model training and discuss the key problems of client selection in Volatile FL. For an efficient settlement, we propose CU-CS, a Combinatorial Multi-Arm Bandit (C2MAB) based decision scheme for the proposed selection problem. Theoretically, we prove that the regret of CU-CS is strictly bounded by a finite constant, justifying its theoretical feasibility. The experimental results demonstrate that our method significantly boosts FL by speeding up model convergence, promoting model accuracy, and reducing energy consumption.

With astonishing speed, bandwidth, and scale, mobile-edge computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large-scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC servers must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we, in this article, propose neural network-based adaptive frequency adjustment (NAFA), an adaptive processor frequency adjustment solution, to enable an effective plan of the server’s energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server’s processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in the average request acceptance ratio and up to 50% reduction in average request processing time.

Age of Information (AoI) has become a new performance metric that quantifies the freshness of information in the Internet of Things (IoT). To optimize the AoI, the latest information should be frequently sampled and timely updated by source nodes (SNs), which, however, contradicts with the fact that the resources of both the SNs and destination node are limited. To consider this issue, this article formulates a more general multisource information update problem, taking into account both the budget constraint of destination node and the limited sampling/updating capabilities of the SNs. Besides that, two different sampling models, namedsampling-predeterminedandsampling-at-willmodels, have been studied, respectively. Particularly, for the information update problem under the sampling-predetermined model, we prove that it is an NP-hard problem. Then, we propose a greedy algorithm to select the appropriate SNs, and theoretically analyze the bound of the AoI achieved by the proposed algorithm. For the sampling-at-will model, we theoretically derive the minimum AoI that can be achieved under given number of updates, based on which, we design an optimal SNs selection and updating time determination mechanism, to achieve the minimum AoI. Finally, we conduct simulations to evaluate the effectiveness of the proposed algorithms.

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, called WS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

Internet-of-Things-enabled E-health system, which could monitor and collect the personal health information (PHI), has gradually transformed the clinical treatment to a more personalized way with in-home monitoring smart devices. Then, with the collected PHI, clinical decision support systems (CDSSs), which are based on data mining techniques and historical electronic medical records (EMRs) to help clinicians make proper treatment decisions, have attracted considerable attention. To address issues, such as network congestion and low rate of responsiveness for traditional methods when implementing CDSSs, we integrate the technologies mobile-edge computing (MEC) and software-defined networking for exploiting the computation resources and storage capacities among edge nodes (ENs) (i.e., MEC servers) in our model. Based on this integrated system, each edge node will deploy a double deep Q-network (DDQN) to obtain a stable and sequential clinical treatment policy. It is enabled by a novel fully decentralized federated framework (FDFF) for aggregating models of DDQN and extracting the knowledge from EMRs across all ENs. Furthermore, we discuss the convergence of FDFF in resource-constrained environments. However, since most EMRs are faced with stringent privacy concerns, we adopt two additively homomorphic encryption schemes to prevent leakage of EMRs' privacy during the training process of FDFF. Finally, we measure the time cost of our additively homomorphic encryption schemes and validate DDQN with experiments on large data sets based on FDFF, which shows promising performance on clinician treatment.

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme.

In mobile cloud computing, offloading resource-demanded applications from mobile devices to remote cloud servers can alleviate the resource scarcity of mobile devices, whereas long distance communication may incur high communication latency and energy consumption. As an alternative, fortunately, recent studies show that exploiting the unused resources of the nearby mobile devices for task execution can reduce the energy consumption and communication latency. Nevertheless, it is non-trivial to encourage mobile devices to share their resources or execute tasks for others. To address this issue, we construct an auction model to facilitate the resource trading between the owner of the tasks and the mobile devices participating in task execution. Specifically, the owners of the tasks act as bidders by submitting bids to compete for the resources available at mobile devices. We design a distributed auction mechanism to fairly allocate the tasks, and determine the trading prices of the resources. Moreover, an efficient payment evaluation process is proposed to prevent against the possible dishonest activity of the seller on the payment decision, through the collaboration of the buyers. We prove that the proposed auction mechanism can achieve certain desirable properties, such as computational efficiency, individual rationality, truthfulness guarantee of the bidders, and budget balance. Simulation results validate the performance of the proposed auction mechanism.

The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A deep learning technique is widely used to analyze the potential value of these data due to its unprecedented performance in both the academic and industrial communities. However, the data generated from the IoT devices are distributed among different users. Directly combining these data to a central server will cause privacy leakage, especially for personal sensitive data. Rather than centralized training by getting access to all these raw data, an alternative is to collaboratively learn a model in a distributed manner. However, there exist two main challenges in a distributed learning setting. The first one is how to preserve the privacy of users. The second one is to reduce the communication burden (e.g., mobile users have limited bandwidth) due to high-frequent data exchange. To address these two challenges, we design a communication efficient and privacy-preserving framework to enable different participants to distributively learn a model with a privacy protection guarantee. In particular, we develop a differentially private approximate mechanism for the distributed deep learning. In addition, we design a new gradient sparsification method to, at the first time, reduce both upload and download communication costs. The performance of the proposed framework is tested under different neural network structures for different data sets including, image classification and mobile sensor data. The experimental results demonstrate that we can reduce the communication up to only 2% compared to the full gradients exchange and achieve up to 16% accuracy increase compared to the previous works.

Despite the increased capabilities of mobile devices, resource-demanded mobile applications still transcend what can be accomplished on a single device. As such, mobile device cloud (MDC), an environment that enables computation-intensive tasks to be performed among a set of nearby mobile devices, offers a promising architecture to support real-time mobile applications. To stimulate mobile devices to execute tasks for others, it is essential to design an incentive mechanism that appropriately charges the owners of the tasks, acted as the buyers, and rewards the mobile devices, acted as the sellers. In this paper, we propose two truthful auction mechanisms for two different task models, heterogeneous and homogeneous task models, which assume the different and the same resource requirements of the tasks, respectively. Specifically, for heterogeneous task model, we propose an efficient heuristic winning bids determination algorithm to allocate the tasks, and decide the payment of each seller for its winning bids. For homogeneous task model, we design an optimal winning bid determination algorithm, and propose a Vickrey-Clarke-Groves (VCG) based auction mechanism to determine the payment of each bid. Both theoretical analysis and simulations show that the proposed auction mechanisms achieve several desirable properties such as individual rationality, truthfulness and computational efficiency.

Energy harvesting communication system enables energy to be dynamically harvested from natural resources and stored in capacitated batteries to be used for future data transmission. In such a system, the amount of future energy to harvest is uncertain and the battery capacity is limited. As a consequence, battery overflow and energy dropping may happen, causing energy underutilization. To maximize the data throughput by using the energy efficiently, a rate-adaptive transmission schedule must address the trade-off between a high-rate transmission which avoids energy overflow and a low-rate transmission which avoids energy shortage. In this paper, we study an online throughput maximization problem without knowing future information. To the best of our knowledge, this is the first work studying the fully-online transmission rate scheduling problem for battery-capacitated energy harvesting communication systems. We consider the problem under two models of the communication channel, a static channel model that assumes the channel status is stable, and a fading channel model that assumes the channel status varies. For the former, we develop an online algorithm that approximates the offline optimal solution within a constant factor for all possible inputs. For the latter, that the channel gains vary in range [hmin; hmax], we propose an online algorithm with a proven ⊖(log(hmax/ hmin))-competitive ratio. Our simulation results further validate the efficiency of the proposed online algorithms.

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications. In order to solve the challenging issue of short-distance interception in secure communications, the benefit of large-scale MIMO (LS-MIMO) relaying techniques is exploited to improve the secrecy performance significantly, even in the case without eavesdropper channel state information (CSI). The focus of this paper is on the analysis and design of optimal power allocation for the relay, so as to maximize the secrecy outage capacity. We reveal the condition that the secrecy outage capacity is positive, prove that there is one and only one optimal power, and present an optimal power allocation scheme. Moreover, the asymptotic characteristics of the secrecy outage capacity is carried out to provide some clear insights for secrecy performance optimization. Finally, simulation results validate the effectiveness of the proposed scheme.

In wireless networks, getting the global knowledge of channel state information (CSI, e.g., channel gain or link loss probability) is always beneficial for the nodes to optimize the network design. However, the node usually only has the local CSI between itself and other nodes, and lacks the CSI between any pair of other nodes. To enable all the nodes to get the global CSI, in this paper, we propose a network-coded third-party information exchange scheme, with an emphasis on minimizing the total transmission cost for ( ) exchanging the CSI among the nodes. We show that for a network of N nodes, if and only if any k nodes (1 ≤ k <; N) send at least (2 : k) packets, a feasible solution exists for third-party information exchange. Formulating the problem of feasible and optimal solutions as an integer linear programming (ILP) problem, we compute the optimal number of packets that must be transmitted by every node. Guided by the necessary and sufficient condition, we construct two practical transmission schemes: fair load (FL) scheme and proportional load (PL) scheme. A deterministic encoding strategy based on XORs coding over GF(2) is further designed to guarantee that with FL or PL scheme, each node finally can decode the complete packets. It is shown that in two specific networks, these two schemes are optimal, achieving the minimum transmission cost. In more general networks, simulation results show that PL is still close to optimal with a high probability. Finally, a distributed transmission protocol is developed, which allows FL and PL schemes to be operated in a distributed and hence scalable manner.

In this paper, we analyze the ergodic sum rate in a multiuser multi-cell downlink. Coordinated beamforming is employed to mitigate the interference, including intra-cell and inter-cell interference. However, due to the limited capacity of the backhaul link, only partial channel state information (CSI) is obtained at the base stations (BSs), resulting in residual interference even with coordinated beamforming. By quantifying the impact of imperfect CSI, we derive closed-form ergodic sum rate results for a multiuser multi-cell downlink in terms of i. CSI accuracy, ii. transmit signal-to-noise ratio (SNR) and iii. channel condition. Furthermore, through asymptotic analysis of the performance loss induced by imperfect CSI, we obtain some clear insights on the performance. Finally, our theoretical claims are validated through extensive simulations.

To upgrade the systems or fix the security issues, some physical machines (PMs) in data centers are required to undergo a maintenance process, which might disable the continuous services of the virtual machines (VMs) run on them for a few time slots. To reduce the waiting delay, one may migrate the VMs to other active PMs. However, it will incur extra migration cost, e.g., bandwidth or memory used to move data. To balance the tradeoff between delay and migration cost, we formulate a two-objective optimization problem, which minimizes both delay and migration cost according to a certain weightage, so as to decide whether the VMs should be migrated to other active PMs or should wait their own maintained PMs to be back. We first prove that the proposed problem is NP-hard. For a special case, where each VM requires the same size of resource, we show that the defined problem can be converted to minimum weighted bipartite matching problem in an auxiliary bipartite graph. A lower bound of the delay is derived for a specific setting. For the general case of the problem, we also design an efficient heuristic algorithm. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

To efficiently archive and query data in wireless sensor networks (WSNs), distributed storage systems, and multisink schemes have been proposed recently. However, such distributed access cannot be fully supported and exploited by existing routing protocols in a large-scale WSN. In this paper, we will address this challenging issue and propose a distributed geographic $(K)$-anycast routing (GKAR) protocol for WSNs, which can efficiently route data from a source sensor to any $(K)$ destinations (e.g., storage nodes or sinks). To guarantee $(K)$-delivery, an iterative approach is adopted in GKAR where in each round, GKAR will determine not only the next hops at each node, but also a set of potential destinations for every next hop node to reach. Efficient algorithms are designed to determine the selection of the next hops and destination set division at each intermediate node. We analyze the complexity of GKAR in each round and we also theoretically analyze the expected number of rounds required to guarantee $(K)$-delivery. Simulation results demonstrate the superiority of the GKAP scheme in reducing the total duration and the communication overhead for finding $(K)$ destinations, by comparing with the existing schemes, e.g., $(K 1)$-anycast [10].

In this paper, we consider the problem of minimizing the total transmission cost for exchanging channel state information. We proposed a network coded cooperative data exchange scheme, such that the total transmission cost is minimized while each client can decode all the channel information held by all other clients. In this paper, we first derive a necessary and sufficient condition for a feasible transmission. Based on the derived condition, there exists a feasible code design to guarantee that each client can decode the complete information. We further formulate the problem of minimizing the total transmission cost as an integer linear programming. Finally, we discuss the probability that each client can decode the complete information with distributed random linear network coding.

In this paper, we dynamically select the transmission rate and design wireless network coding to improve the quality of services such as delay for time critical applications. With low transmission rate, and hence longer transmission range, more packets may be encoded together, which increases the coding opportunity. However, low transmission rate may incur extra transmission delay, which is intolerable for time critical applications. We design a novel joint rate selection and wireless network coding (RSNC) scheme with delay constraint, so as to minimize the total number of packets that miss their deadlines at the destination nodes. We prove that the proposed problem is NP-hard, and propose a novel graph model and transmission metric which consider both the heterogenous transmission rates and the packet deadline constraints during the graph construction. Using the graph model, we mathematically formulate the problem and design an efficient algorithm to determine the transmission rate and coding strategy for each transmission. Finally, simulation results demonstrate the superiority of the RSNC scheme.

Service-oriented wireless sensor network (WSN) has been recently proposed as an architecture to rapidly develop applications in WSNs. In WSNs, a query task may require a set of services and may be carried out repetitively with a given frequency during its lifetime. A service composition solution shall be provided for each execution of such a persistent query task. Due to the energy saving strategy, some sensors may be scheduled to be in sleep mode periodically. Thus, a service composition solution may not always be valid during the lifetime of a persistent query. When a query task needs to be conducted over a new service composition solution, a routing update procedure is involved which consumes energy. In this paper, we study service composition design which minimizes the number of service composition solutions during the lifetime of a persistent query. We also aim to minimize the total service composition cost when the minimum number of required service composition solutions is derived. A greedy algorithm and a dynamic programming algorithm are proposed to complete these two objectives respectively. The optimality of both algorithms provides the service composition solutions for a persistent query with minimum energy consumption.

Nowadays, Fiber-Wireless (FiWi) network is proposed as a hybrid access network that integrates optical access networks (e.g., PONs) with wireless access networks (e.g., WMNs) to provide the high bandwidth, cost-efficient and ubiquitous last mile Internet access. In FiWi networks, besides traffic from wireless mesh clients to the Internet, peer-to-peer communication from one wireless client to another wireless client is introduced due to the recent growth of applications such as multimedia transmissions within community areas. In FiWi networks, such peer-to-peer traffic can be carried either through the wireless path within the wireless mesh subnetwork or through the wireless-optical-wireless mode in which traffic firstly goes from the source client to its closest ONU, and then goes to the ONU closest to the destination client through the PON subnetwork and finally reaches the destination client. Such wireless-opticalwireless mode for peer-to-peer communications can alleviate interferences in the wireless subnetwork, thus improving the network throughput. Considering such mode for peer-to-peer communications, ONUs' placement will have great impact on the achievable network throughput in FiWi networks and will be different from the placement when only traffic to the Internet is considered. In this paper, given the distribution of wireless mesh routers, we study where to place K ONUs in FiWi networks so that the overall network throughput can be maximized when peer-to-peer communications are considered in addition to traffic destinated to the Internet. We first formulate the problem and then propose a tabu search (TS) based heuristic to solve the problem. Simulation results show that compared to the random deployment and the fixed deployment which performs well when only traffic to the Internet is considered, the tabu search heuristic has a much better performance.