Medium access control (MAC) is crucial for resource allocation in wireless body area networks (WBANs). However, existing MAC protocols often suffer from transmission conflicts and inefficient channel utilization. To address these issues, this paper proposes a Node-Differentiated Resource Scheduling (NDRS) MAC protocol, which dynamically allocates access resources based on node-specific requirements. This protocol employs a superframe structure consisting of a contention-based phase and a contention-free phase for data transmission. A Mamdani fuzzy inference system is utilized to calculate continuous node priorities. These priorities achieve fine-grained differentiation of node importance and thus serve as the foundation for transmission conflict minimization. During the contention-based phase, continuous and differentiated backoff times are assigned to nodes based on their priorities. These backoff times effectively reduce transmission collisions and enhance channel utilization. In the contention-free phase, time slots are preferentially allocated to nodes with higher priority, better channel utilization, and greater transmission reliability. This allocation thereby enhances channel usage efficiency and reduce transmission delays. This protocol is characterized by three key features: precise node prioritization, low transmission collisions, and high channel utilization. Extensive experimental results demonstrate that NDRS outperforms existing protocols in terms of average delay, throughput, packet loss ratio, and average energy consumption.

Federated Learning (FL) enhances data privacy for End Equipment Workers (EWs) by enabling the sharing of model parameters instead of raw data. However, energy constraints and individual self-interest may discourage EWs from participating or slow down training, ultimately affecting the performance of the global FL model. To address these challenges, we propose a three-stage Stackelberg game-based framework that leverages wireless power to incentivize participation while ensuring the successful completion of FL tasks. In this framework, the Base Station (BS) publishes FL task and seeks to obtain an improved global model at a reduced cost. EWs train local models, aiming to maximize their payments while minimizing energy consumption. Meanwhile, the Charging Service Provider (CSP) supplies energy to EWs via Wireless Power Transfer (WPT) during model training and uploading, charging appropriate fees for the service. We employ the backward induction method to analyze the proposed game problem, proving the existence of a unique Stackelberg equilibrium and Nash equilibrium. Furthermore, we propose the Trust Region Method (TRM) to solve the unit payment strategy problem of BS. Extensive simulations validate that our method consistently outperforms benchmark schemes, achieving higher average utility across a wide range of scenarios.

Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this article, we propose a novel connectivity restoration scheme to address this problem. This scheme comprises three connectivity mechanisms regarding relay segment selection in different regions. The first one is a data traffic decentralization mechanism, which establishes more transmission paths near the sink for reliability improvement and traffic load balancing. The second one is a segment shape selection mechanism, in which the segments with high-reliability preferably become the relay segments for greater network robustness. The third one is a traffic load transfer mechanism, in which data traffic is transferred from a high-load segment to a low-load segment for balancing energy depletion of the network. The distinctive characteristics of this work are twofold: different regions perform diverse connectivity restoration approaches according to the demand diversity of different regions, and traffic load can be balanced from upstream regions rather than only from downstream regions. Extensive simulation experiments validate the effectiveness and advantages of our proposed scheme in terms of connection cost, network robustness, load balance degree, and network longevity.

Broadcast plays a significant role in the emergency message propagation in Vehicular Ad hoc Networks (VANETs). However, current broadcast relay strategies easily cause serious message loss and larger broadcast costs due to the poor environmental adaptability. In this paper, to handle the above problems, we propose an Adaptive Connectivity-Aware Relay (ACAR) strategy, which has two striking features: multiple network connectivity states and dynamic broadcast relay policies. We design four network connectivity states and their corresponding four broadcast relay policies. In disconnected networks, only the vehicle with the same movement direction as the message propagation direction acts as the relay, so as to relieve the message loss. Moreover, different broadcast periods are allocated for different applications, so as to reduce the broadcast costs. In connected networks, the link quality and transmission distance are adopted to select the relay in the sender-based relay pattern, so as to address the relay invalidation problem. Further, different candidate relays are assigned different broadcast priorities and different broadcast waiting time in the receiver-based relay pattern, so as to address the transmission conflict problem. Simulation results show that ACAR outperforms existing competing schemes in terms of end-to-end delay, broadcast success rate, and packet overhead.

By sharing model parameters instead of raw data to train machine models, Federated Learning (FL) can protect End equipment Workers (EWs)’ data privacy. However, due to energy constraints and selfishness, EWs may not be willing to participate or train slowly, which affects the performance of global FL model. To address these issues, we propose a three-stage Stackelberg game-based wireless powered FL framework to incentivize all players to participate in the system while ensuring the successful completion of FL tasks. Specifically, Base Station (BS) publishes the FL task and wants to obtain a better FL model at a lower cost. EWs train local FL models, and want to get more payment with less energy consumption. When EWs train and upload their local models, Charging Service Provider (CSP) transmits energy to them via Wireless Power Transfer (WPT) while charging fees. In order to obtain the optimal strategy for all participants, we analyze the proposed game problem using the backward induction method. Meanwhile, we prove that the unique Stackelberg equilibrium and Nash equilibrium can be obtained, and we obtain the approximate optimal solution of BS using the subgradient method. Finally, extensive simulations are conducted to evaluate the performance of the proposed method in different scenarios. The results show that the proposed method improves the utility of three parties by an average of 19.09% - 51.86% compared with the benchmark methods.

Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.

Benefitting from the maturation of Wireless Power Transfer technology, Wireless Rechargeable Sensor Networks have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this article, we study a fundamental issue of W ireless ch A rger placement w I th obs T acles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1-1/e-ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.

Charging scheduling plays a crucial role in ensuring durable operation for wireless rechargeable sensor networks. However, previous methods cannot meet the strict requirements of a high node survival rate and high energy usage effectiveness. In this article, we propose a multi-type charging scheduling strategy to meet such demands. In this strategy, the network is divided into an inner ring and an outer ring to satisfy different demands in different areas. The inner ring forms a flat topology, and adopts a periodic and single-node charging pattern mainly for a high node survival rate. A space priority and a time priority are designed to determine the charging sequence of the nodes. The optimal charging cycle and the optimal charging time are achieved by mathematical derivations. The outer ring forms a cluster topology, and adopts an on-demand and multi-node charging pattern mainly for high energy usage effectiveness. A space balancing principle and a time balancing principle are designed to determine the charging positions of the clusters. A gravitational search algorithm is designed to determine the charging sequence of the clusters. Several simulations verify the advantages of the proposed solution in terms of energy usage effectiveness, charging failure rate, and average task delay.

The multi-hop broadcast is of crucial significance to emergency message dissemination in vehicular ad hoc networks (VANETs). However, current solutions focus on a single and fixed goal, which cannot satisfy the dynamic and diverse emergency message requirements. In this article, we propose an adaptive broadcast-relay selection scheme to fill up this gap. An adaptive control message is designed to reflect the dynamic and diverse emergency message requirements. An adaptive relay pattern switching mechanism is designed to accommodate such requirements based on the adaptive control message. Delay-sensitive messages are broadcasted by an adaptive sender-based relay pattern, in which the link quality is used to address the inherent transmission reliability problem. Delay-insensitive messages are broadcasted by an adaptive receiver-based relay pattern, in which the time interval of adjacent potential relays is set to address the inherent packet collision problem. The unique features of our solution are twofold. One is the stronger adaptivity due to the implementation of the dynamic relay patterns, and the other is the wider applications due to the satisfaction of multiple types of emergency messages. Extensive simulations demonstrate the advantages of our solution in terms of transmission delay, dissemination speed, and adaptability in different scenarios.

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks.

Interference is a serious problem in Wireless Body Area Networks (WBANs) and heavily weakens system performance. In this paper, we propose an exchange-free resource scheduling scheme to overcome the interference of dynamic coexisting WBANs. For each data transmission period, we design a transmission channel/slot allocation scheme based on a Latin square, where each character denotes a specific combination of a channel and a time slot. For each data retransmission period, we design a retransmission time-slot selection scheme based on a hash function, in which the unique identity information of the collided node is used to calculate the retransmission slot. Compared with existing solutions, our work has two key advantages. First, all nodes can independently allocate and coordinate resources rather than exchange information with each other in traditional methods, and thus guaranteeing strong adaptability to the fast changes of WBANs. Second, the contention-free resource allocation pattern is implemented for both the data transmission period as well as the data retransmission period, and thus guaranteeing no intra-WBAN interference and extremely low probability of inter-WBAN interference. Our simulation results show that interferences can be well addressed based on the metrics of the packet loss rate, throughput, power dissipation, and data delivery delay.

In wireless sensor networks (WSNs), the imbalanced energy consumption in data transmission may cause energy holes around the sink, which dramatically shortens the network lifespan. Current solutions have two limitations: one is the inevitable traffic gathered around the sink, and the other is the overly ideal network model, e.g., the square region or circular area. In this article, we focus on strip-shaped networks and propose a novel data transmission scheme, which employs a few super nodes near the sink to take traffic load. Due to the high energy capacities and communication abilities, super nodes transfer a part of the data of the network and send it to the sink directly. The entire network is partitioned into multiple clusters, and super nodes are placed in a specific cluster. We discover that the greatest effect on the network lifetime is the energy consumption of two clusters: 1) the cluster nearest to the sink and 2) the upstream cluster nearest to the super nodes. To improve the system longevity, we make the two clusters have equal energy dissipation, and thus obtain the optimal location of super nodes. Some simulations are carried out to verify the reasonability of the results and exhibit the advantage of our scheme in terms of network lifetime.

Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G architecture, which can empower user equipments (UEs) with computation and energy resources offered by migrating workloads from UEs to the nearby MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly focus on facilitating the performance in the quasistatic system, and seldomly consider time-varying system conditions in the time domain. In this article, we investigate the joint optimization of computation offloading and resource allocation in a dynamic multiuser MEC system. Our objective is to minimize the energy consumption of the entire MEC system, by considering the delay constraint as well as the uncertain resource requirements of heterogeneous computation tasks. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem, and propose a value iteration-based reinforcement learning (RL) method, named$Q$-Learning, to determine the joint policy of computation offloading and resource allocation. To avoid the curse of dimensionality, we further propose a double deep$Q$network (DDQN)-based method, which can efficiently approximate the value function of$Q$-learning. The simulation results demonstrate that the proposed methods significantly outperform other baseline methods in different scenarios, except the exhaustion method. Especially, the proposed DDQN-based method achieves very close performance with the exhaustion method, and can significantly reduce the average of 20%, 35%, and 53% energy consumption compared with offloading decision, local first method, and offloading first method, respectively, when the number of UEs is 5.

Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g., cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant for the ever-increasing information requirements to promote the quality of citizens’ life. In this article, we design an information prefetching architecture, which investigates a hierarchical data storage and selection strategy, including local to edge and edge to cloud. Building on collected data in the social media system or sensor networks, we specifically focus on analyzing mobile terminals’ behaviors to assure the precision of our prefetching strategy in different kinds of information streaming. To assemble edge agents (EAs) prefetching, we also consider the characteristics of wireless backhaul. This scheme is carried out to optimize the EAs prefetching framework by the independent and joint action modules that are based on the theory of deep reinforcement learning (DRL). It paves a better way of collaborative edge computing (CEC) that can be built by using an independent/joint edge-learning model to help and promote the algorithm efficiency and cost-effectiveness. Furthermore, for hiding the information of data transmission between the cloud and the edge servers during data prefetching, this hierarchical scheme is designed as an implicit index maintained by edge servers. Our results show rationales on the obtainable performance of EAs architectures and their reciprocity with the dynamic change of mobile terminals’ requirements.

Data collection with mobile elements can improve energy efficiency and balance load distribution in wireless sensor networks (WSNs). However, complex network environments bring about inconvenience of path design. This work addresses the network environment issue, by presenting an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned WSNs. Unlike existing studies of connected networks, our work focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios respectively. We first design a converging-aware location selection mechanism, which macroscopically converges rendezvous points (RPs) to lay a foundation of a short tour. We then develop a delay-aware path formation mechanism, which constructs a short tour connecting all segments by a new convex hull algorithm and a new genetic operation. In addition, we devise an energy-aware path extension mechanism, which selects appropriate extra RPs according to specific metrics in order to reduce the energy depletion of data transmission. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio.

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the ‘freshness’ of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay$B$, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than$B$. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio$4\frac {4}{7}$for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average.

In inhospitable environments, connectivity recovery plays a significant role in enabling normal data transmission in wireless sensor networks (WSNs). However, existing approaches lack thorough load-balancing and delay-control functions. In this article, we present a load-balanced connectivity recovery (LBCR) strategy to address these problems. This strategy consists of two relay-segment selection approaches. The first one is the delay-controlled connectivity mechanism, in which mobile relays and static relays are employed to connect isolated segments, and the path length of the two kinds of relays is adjusted based on the requirement of data delivery delay. The second one is the load-balanced connectivity mechanism, in which both the intra-segment and the inter-segment traffic distribution are evaluated to balance the traffic load. For intra-segment load equilibrium, we use the A-Star algorithm to calculate the number of disjoint paths of a relay segment, which helps to evaluate the load sharing ability from a micro perspective. For inter-segment load equilibrium, we compare the traffic load of different paths, which helps to evaluate the load sharing ability from a macroscopic angle. Extensive simulations demonstrate the effectiveness and advantage of our strategy in terms of connectivity cost, data collection delay, and system lifespan.

Medium access control (MAC) plays a crucial role in ensuring proper operation in vehicular ad hoc networks (VANETs). However, existing solutions cannot meet the strict requirements of high throughput and low latency in high-density scenarios. In this paper, we propose a novel MAC protocol to meet such demands for VANETs. The roadside unit (RSU) pattern and the dual-transceiver manner are adopted to assign channel resources for all packets. The striking features of our approach are twofold: the time slot allocation based on global vehicle information and the consideration of different delay requirements of different applications. First, we design a time slot exchange mechanism, which avoids any transmission conflict between two adjacent RSUs, to reduce channel resource waste. Then, we design a packet weight allocation mechanism, by which the packets of any network edge area are assigned higher priorities to further reduce channel resource waste. Moreover, we devise a packet quality evaluation mechanism, by which our multi-objective problem is transformed into a single-objective problem. In addition, we devise a greedy branch-and-bound algorithm to address the multiple-knapsack problem, which is transformed into a single-knapsack problem. Extensive simulation results show the advantages of our approach in terms of throughput and latency.

Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN). However, relatively low charging utility exists in existing solutions. In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs. Charging MCs and server MCs are employed to jointly achieve our goal by three aspects. First, a path merging scheme is designed to save the traveling paths of MCs. Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas. Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs. Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs. Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs. The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles. Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

Mobile Edge Computing (MEC) has emerged as a promising computing paradigm in 5G networks, which can empower User Equipments (UEs) with computation and energy resources offered by migrating workloads from the UEs to the MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly investigate quasi-static system environments, without considering the different resource requirements and time-varying system conditions in a dynamic system. In this paper, we exploit a multi-user MEC system, and investigate the task execution scheme for dynamic joint optimization of offloading decision and resource assignment. Our objective is to minimize the energy consumption of all UEs, with considering the delay constraint as well as the dynamic resource requirements of heterogeneous computation tasks. Accordingly, we formulate the problem as a mixed integer non-linear programming problem (MINLP), and propose a value iteration based Reinforcement Learning (RL) approach, named Q-Learning, to obtain the optimal policy of computation offloading and resource allocation. Simulation results demonstrate that the proposed approach can significantly decrease UEs' energy consumption in different scenarios, compared with other baseline methods.

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) is an effective way to relieve the burden of cellular networks. Providing data offloading services requires a lot of resources, and nodes in OMNs are selfish and rational, they are not willing to provide data offloading services for others without any compensation. Therefore, it is urgent to design an incentive mechanism to stimulate mobile nodes to participate in data offloading process. In this paper, we propose a Reverse Auction-based Incentive Mechanism to stimulate mobile nodes in OMNs to provide data offloading services, and take the cache management into consideration. We model the incentive-driven data offloading process as a non-linear integer programming problem, then a Greedy Helper Selection Method (GHSM) and a Caching Replacement Scheme (CRS) are proposed to solve the problem. In addition, we also propose an innovative payment rule based on the Vickrey-Clarke-groves (VCG) model to ensure the individual rationality and authenticity of the proposed algorithm. Trace-driven simulation results show that the proposed algorithm can reduce the cost of Content Service Provider (CSP) significantly in different scenarios.

Data shuffling can improve the statistical performance of distributed machine learning. However, the obstruction of applying data shuffling is the high communication cost. Existing works use coding technology to reduce communication cost. These works assume a master-worker based storage architecture. However, due to the demand for unlimited storage on the master, the master-worker storage architecture is not always practical in common data centers. In this paper, we propose a new coding method for data shuffling in the decentralized storage architecture, which is built on a fat-tree based data center network. The method determines which data samples should be encoded together and from which the encoded package should be sent to minimize the communication cost. We develop a real-world test-bed to evaluate our method. The results show that our method can reduce the transmission time by 6.4% over the state-of-art coding method, and by 27.8% over Unicasting.

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.

Connectivity restoration plays an important role in maintaining the long-term operation in wireless sensor networks (WSNs), especially, in environment-harsh cases. However, current solutions lack the ability to handle the second damages and the capacity of designing requirement-different connectivity approaches according to different needs. In this article, we propose a durability-based connectivity establishment (DBCE) scheme for disjoint segments of WSNs. This scheme includes three approaches regarding segment evaluation or segment selection: 1) a segment shape evaluation approach; 2) a region different connectivity approach; and 3) a data traffic transfer approach, for their respective objectives. The unique characteristics of this article are twofold. On the one hand, this is the first attempt to investigate segment shapes, which we demonstrate have great impact on the robustness of the network. On the other hand, distinguished from the existing networks with uniform connectivity rule, the network is divided into two parts and different connectivity sequences and connectivity approaches are designed according to disparate features and requirements of the network. The performance of DBCE is validated through extensive simulation experiments.

Data collection with mobile elements can greatly improve the load balance degree and accordingly prolong the longevity for wireless sensor networks (WSNs). In this pattern, a mobile sink generally traverses the sensing field periodically and collect data from multiple Anchor Points (APs) which constitute a traveling tour. However, due to long-distance traveling, this easily causes large latency of data delivery. In this paper, we propose a path planning strategy of mobile data collection, called the Dual Approximation of Anchor Points (DAAP), which aims to achieve full connectivity for partitioned WSNs and construct a shorter path. DAAP is novel in two aspects. On the one hand, it is especially designed for disconnected WSNs where sensor nodes are scattered in multiple isolated segments. On the other hand, it has the least calculational complexity compared with other existing works. DAAP is formulated as a location approximation problem and then solved by a greedy location selection mechanism, which follows two corresponding principles. On the one hand, the APs of periphery segments must be as near the network center as possible. On the other hand, the APs of other isolated segments must be as close to the current path as possible. Finally, experimental results confirm that DAAP outperforms existing works in delay-tough applications.

The traditional many-to-one transmission pattern allows all sensor nodes to propagate their packets toward a single sink in wireless sensor networks, resulting in imbalanced energy depletion in the network. In this paper, we propose a data dissemination strategy named transmission with multiple load balancing schemes (TMLBSs) which utilizes a nature-inspired approach, ant colony optimization, to construct transmission paths for nodes in different places. The distinct characteristics of TMLBS are three load balancing schemes, which help to construct transmission paths formed into a path tree. The first one is the load decentralization scheme, which creates multiple path subtrees in the early stage and scatters the whole load to such path subtrees, so as to avoid excessive load concentration. The second one is the load maintenance scheme, which adopts an appropriate pheromone update mechanism to retain previous good paths, yielding excellent next-generation solutions. The last one is the load diversion scheme, which employs the heuristic factor to transfer traffic load to paths with light traffic load to eliminate poor solutions. Finally, extensive simulations are conducted to validate the effectiveness and advantages of the new transmission strategy.

The broadcast channel with mutual secrecy requirement at the receivers (BC-MSR-R) is a basic model characterizing the physical layer security (PLS) in the down-link of the wireless communication systems, where one transmitter sends two independent messages to two receivers via a broadcast channel, and each receiver can successfully decode his/her intended message and wishes to overhear the other one's message. This paper studies how to enhance the already existing secrecy rate region of the BC-MSR-R via receivers' feedback. Specifically, we propose two feedback strategies for the BC-MSR-R, where one uses the feedback to generate pure secret keys protecting the transmitted messages, and the other uses the feedback to generate not only keys but also cooperative messages helping the receivers to improve their decoding performance. Different inner bounds on the secrecy capacity region of the BC-MSR-R with noiseless feedback are constructed according to different feedback strategies, and these bounds are further illustrated by a Dueck-type example.

Node deployment is one of the most crucial issues in wireless sensor networks, and it is of realistic significance to complete the deployment task with multiple types of application requirements. In this paper, we propose a deployment strategy for multiple types of requirements to solve the problem of deterministic and grid-based deployment. This deployment strategy consists of three deployment algorithms, which are for different deployment objectives. First, instead of general random search, we put forward a deterministic search mechanism and the related cost-based deployment algorithm, in which nodes are assigned to different groups which are connected by near-shortest paths, and realize significant reduction of path length and deployment cost. Second, rather than ordinary nondirection deployment, we present a notion of counterflow and the related delay-based deployment algorithm, in which the profit of deployment cost and loss of transmission delay are evaluated, and achieve much diminishing of transmission path length and transmission delay. Third, instead of conventional uneven deployment based on the distances to the sink, we propose a concept of node load level and the related lifetime-based deployment algorithm, in which node distribution is determined by the actual load levels and extra nodes are deployed only where really necessary. This contributes to great improvement of network lifetime. Last, extensive simulations are used to test and verify the effectiveness and superiority of our findings.