Device-to-device (D2D) communication improves the performance and flexibility of mobile networks. It enables direct communication between two user equipments (UEs) without the relay via a base station (BS). D2D pairs can share the spectrum resources given to cellular UEs, but doing so causes interference. Thus, how to manage resources and transmitted power for UEs is important. This paper proposes a D2D resource allocation and power control with RAN sharing (DACS) framework, where some service providers (SPs) each possess a part of the BS’s resources. Each SP allots its resources to UEs and decides power to satisfy UEs’ SINR demands while reducing interference. SPs can borrow resources from each other for serving more UEs. Then, DACS amplifies the power of senders adaptively to better signal quality. Simulation results reveal that DACS can achieve a high service rate, increase energy efficiency, and keep fairness among SPs.

The severity of air pollution has prompted increased attention to air pollution monitoring. This paper uses unmanned aerial vehicles (UAVs) to discover chimneys with excessive exhaust emissions (called 3E-chimneys) in an industrial area. An efficient path-planning framework is proposed to find the flight path of a UAV for checking out chimneys and determine sampling points (SPs) to collect data. The framework builds the shortest path to visit chimneys. Four schemes are designed to select SPs around each chimney. The dual-location sampling (DLS) scheme chooses two SPs for each chimney according to its height. The upward spiral sampling (USS) scheme lets the UAV spiral upward at each chimney to gather data. In the inverted-U sampling (IUS) scheme, the UAV flies up to visit downwind SPs and then flies down to pass upwind SPs. The asynchronous isometric sampling (AIS) scheme picks an upwind SP and multiple downwind SPs. On doing the monitoring task, the UAV checks if some chimneys are skippable (i.e., not 3E-chimneys) using the industrial source complex (ISC3) model. In this way, the UAV can expedite the monitoring task and save energy. Simulation results reveal that the AIS scheme strikes a good balance between cost and performance for the monitoring task. The cost is defined by the length of the UAV’s flight path. Four metrics are adopted to evaluate the performance: accuracy, recall, precision, and F1-score. Moreover, we make a prototype system to show the feasibility of our framework, which measures the concentration of CO2 gases emitted from small chimneys in a micro-field with a single wind direction to recognize 3E-chimneys.

Narrowband Internet of Things (NB-IoT) offers three coverage enhancement (CE) levels to serve massive machines in a large area. For each CE level, the base station configures control periods to determine the number of allocatable radio resources for signal and data transmissions. Both uplink (UL) and downlink (DL) communications use the same control periods, but UL and DL machines need different period lengths. In general, a control period suitable for the UL is longer than that for the DL. Then, the base station assigns UL and DL resources in control periods to each machine. To this end, we study how to choose a suitable length of control periods and allot radio resources to UL and DL machines to minimize resource consumption, thereby improving NB-IoT performance. Two efficient algorithms are thus proposed. Based on the CE level, the control period adaptation algorithm flexibly adjusts control periods using a scale factor. The joint UL and DL resource allocation algorithm distributes radio resources in each control period among machines to increase utilization. Simulation results demonstrate that our algorithms can efficiently decrease the consumption of UL and DL subframes, especially for machines with bad channel qualities.

Kubernetes is a powerful tool to manage containerized applications, which is also regarded as one promising platform to support microservices in edge computing. The scheduler is a key component of Kubernetes. It allocates each pod (i.e., a set of running containers) to one worker node (i.e., a machine). The default scheduler in Kubernetes is designed for the cloud environment containing homogeneous nodes. However, IoT edge nodes usually have various computing power and network bandwidth. This article proposes a delay-aware container scheduling (DACS) algorithm to address the issue of node heterogeneity in edge computing. To efficiently assign pods to worker nodes, DACS takes account of not only residual resources of worker nodes but also potential delays caused by the pod assignment. We build a Kubernetes cluster by VMware to evaluate system performance. Experimental results reveal that DACS can significantly reduce both processing and network delays, thereby helping Kubernetes perform more efficiently in an edge environment.

Network function virtualization (NFV) is a technique to facilitate service deployment by decoupling network functions from dedicated hardware and moving them to software, namely virtual network functions (VNFs). Network services are carried out by service function chains (SFCs) comprising multiple VNFs. Given SFCs and physical machines (PMs), the VNF deployment problem asks how to assign each SFC's VNFs to PMs, such that the service ratio is maximized. Many methods use as few PMs as possible for deploying VNFs, but they lead to imbalanced loads of PMs in a cloud network. This paper proposes a load-balanced VNF deployment (LBVD) scheme, which scores each PM based on the expected ratios of residual resources of that PM and its neighbors. Then, VNFs are deployed on PMs with high scores. If a PM is busy, LBVD lets its VNF migrate to another PM for load sharing. Through simulations, we show that LBVD can balance the loads of PMs and save both deployment and migration costs.

To fulfil the rapid growth of traffic demands and diverse network applications, 5G base stations (called gNBs) are being widely deployed. Due to some activities like sport events, the service area would be crowded with many user equipments (UEs). Thus, gNBs may not have enough spectrum resources to serve UEs, leading to low throughput and high packet loss. This paper uses a few mobile cell sites (MCSs) to solve the problem, which are gNBs equipped on vehicles and can flexibly move to serve UEs. We propose an efficient resource scheduling and MCS dispatch (ESMD) scheme, which helps each gNB allot resources to UEs to raise throughput. Then, we pick candidate locations for MCSs to move to help share loads of busy gNBs. Specifically, we adopt the Pareto-optimal concept to find one-to-one assignments between MCSs and these locations. Through simulations, we show that the ESMD scheme efficiently improves network performance in terms of throughput, packet loss, and the outage ratio.

Abstract Device‐to‐device (D2D) relay not only broadens the service coverage of a base station (BS) but also raises network capacity. When the channel quality of a user equipment (UE) is not good, it requests another UE to relay its data from the BS. Since many UEs are owned by self‐interested users, token‐based incentive (TBI) mechanisms allow UEs selling and buying relay services by exchanging tokens, thereby pricking UEs on to act as relay nodes. However, such mechanisms are vulnerable to the miser problem , where malicious UEs keep collecting tokens from others but never spend tokens. Eventually, negotiable tokens will gradually decrease, so more and more UEs have very few tokens to buy relay services, which restrains D2D relay. To conquer the miser problem, this paper proposes three token circulation strategies, which tax UEs and redistribute the taxed tokens to those UEs in need. Thus, misers can be prevented from hoarding tokens and breaking a TBI mechanism. The proposed strategies need not add tokens to the network, so they will not cause inflation of tokens. Simulation results show that these strategies can keep high D2D throughput with the existence of misers, which protects TBI mechanisms and promotes the performance of D2D relay.

Software-defined networking (SDN) makes network management easier by using a controller to govern all switches, but the controller may become a performance bottleneck. Distributed SDN control is a promising solution, which lets multiple controllers divide the work, where each controller manages a part of the network. Switch migration is one common means to the load balance of controllers, which transfers some switches to different subnets based on the workloads of their controllers. The paper proposes a time-sharing switch migration (TSSM) scheme to provide more refined load sharing for controllers, which allows two controllers to share a switch’s load sequentially in the same period. When a controller is overloaded, TSSM finds assistant controllers to share its workload by selecting proper switches for migration and also deciding the time to perform migration. In this way, the workload of each controller can be kept below a given threshold. We implement the TSSM scheme on the open network operating system (ONOS) to attest to its feasibility. Experimental results show that TSSM can reduce 98% of the occurrences of overload for controllers as compared with the original OpenFlow method. Moreover, TSSM can save about 78% of the migration cost than the churn-triggered migration method.

Wireless sensor networks (WSNs) provide long-term monitoring of the environment but sensors are powered by small batteries. To extend WSN lifetime, using a mobile charger (MC) to visit sensors and charge their batteries is a promising solution. Though many approaches are developed to find the MC's visiting path, they usually assume that sensors have a fixed sensing rate (SR) and prefer fully charging sensors. In practice, SRs can be adaptive, as sensors may adjust their SRs due to application needs or energy saving. Moreover, with the fully charging policy, some low-energy sensors take long to wait for the MC's service. Thus, the paper formulates a path and charge (P&C) problem, which asks how to dispatch the MC to visit sensors with adaptive SRs and decide the time to charge them, such that their survivability and throughput can be maximized. An efficient P&C scheduling (EPCS) scheme is proposed to solve the P&C problem by building a shortest path to visit each sensor. Afterward, some energy-rich sensors may be excluded from the path to help the MC fast move to charge those near death. EPCS also adopts a floating charging strategy based on the ratio of workable sensors and their energy depletion. Simulation results show that EPCS can significantly improve both survivability and throughput of sensors.

Internet-of-Things (IoT) devices are widely deployed and many of them have limited capabilities. Constrained application protocol (CoAP) is developed to integrate such devices into the Web environment. Specifically, client devices register their interested data with IoT devices, which will keep notifying them of the status of sensing data. However, client devices have different demands in terms of minimum and maximum periods to receive notifications, whereas IoT devices usually offer notifications at regular intervals. Some IoT devices would send many more notifications than necessary, which wastes bandwidth and energy. To conquer this problem, this article proposes a group-based message management (GMM) framework for CoAP proxies to efficiently coordinate the sending of notifications from IoT devices and forward them to client devices. GMM curtails superfluous notifications by three modules. The scheduling module groups client devices based on their demands and finds an optimal observation period for each group. The caching module adjusts the max-age value to allow a proxy reusing its cached notifications to answer requests. The merging module combines the notifications originated from different IoT devices queried by the same client device so as to save bandwidth. The simulation results show that GMM not only reduces unnecessary notifications but also conserves the energy of devices.

Wireless sensor networks are popularly used in IoT applications. It is essential to save energy of sensors on reporting data. Though many routing methods have been developed, how to well integrate packet routing with data compression is not much discussed. Hence, we propose an event-aware hierarchical routing with differential compression (EHR-DC) scheme. It groups sensors and selects a cluster head (CH) in each group to manage routing and compression. In normal times, sensors transmit data to their CHs, which are condensed by exploiting spatial correlation. When events appear, sensors adaptively forward data to nearby CHs to raise the efficiency of compression. Through simulations, we show that EHR-DC outperforms other methods in terms of network lifetime and the amount of sensing data retrieved by the sink.

To satisfy the growing demand of wireless access, a mass of small cells are deployed in the service area to intensify signal quality and team up with macrocells. However, it spends lots of energy to keep the operation of small cells, which collides with the goal of green communications. In the paper, we propose an efficient load sharing (ELS) scheme to conquer this problem by transferring services of user equipments (UEs) among different cells. For each small cell in the off-peak period, its serving UEs will be adaptively taken over by other cells through handover or user-to-network relay (i.e., D2D relay). Thus, its base station can switch to the sleep mode and save energy. The above mechanism is also applied to the small cells whose base stations are overloaded for mitigating congestion. Simulation results show that ELS raises energy efficiency, curtails energy expense of the base stations in small cells, and provides better QoS support for UEs.

Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods.

Ethernet is the predominant protocol in the data-link layer of wired networks. It works based on the concept of broadcast domain by using switches to connect hosts, which means that every host in a local area network (LAN) will receive packets sent from other nodes. Thus, Ethernet's performance inevitably degrades when LAN scale grows, as the LAN will be congested by superfluous packets due to broadcast. Moreover, Ethernet switches cannot support multicast protocols but realize them by also broadcasting packets, which worsens the situation. To solve the broadcast problem, we develop a low-cost, high-efficiency framework based on software defined network (SDN), which aims to diminish redundant traffics produced by the address resolution protocol (ARP) and the Internet group management protocol (IGMP). Through simulations by Mininet, we show that our SDN framework not only greatly reduces broadcast packets in Ethernet but also significantly saves the controller's cost as comparing with other SDN-based methods. In addition, the proposed framework is also implemented in our campus network to show its effectiveness.

A data center network (DCN) is composed of many servers interconnected by well-organized switches, which involves massive amount of data transmissions to provide cloud services. It is critical to manage packet routes in DCNs to avoid congestion on some links. The software-defined networking (SDN) technique supports auto-configuration of switches by a central controller, and gives an easy way to manage traffic flows. In the paper, we exploit SDN to improve performance of fat-tree DCNs, and propose a low-cost, load-balanced route management (L2RM) framework. L2RM keeps monitoring network traffics and computes a load-deviation parameter to check if some links of switches are burden with heavy loads. Then, an adaptive route modification mechanism is triggered if necessary, which considers the size limitation of flow tables and uses group tables in OpenFlow to distribute flows among different links to balance their loads. Besides, L2RM uses a dynamic information polling mechanism to query switches about their statuses, so as to reduce the message overhead of the controller. Through Mininet simulations, we show that our L2RM framework can better increase link utilization, alleviate table overflow, and reduce message cost, as comparing with other SDN-based approaches for fat-tree DCNs.

Long term evolution-advanced (LTE-A) supports high-speed communications for cellular systems. It allows different types of base stations to form a heterogeneous network to provide service. However, signal interference between a macrocell and its picocells is inevitable. Thus, LTE-A adopts enhanced intercell interference coordination (eICIC) to conquer the interference problem. In the time-domain eICIC strategy, a number of subframes are selected as almost blank subframes (.ABSs) during which data transmission of the macrocell is suspend, so as to improve the utilization of spectral resource in picocells. How to configure the number of ABSs in each period, namely the ABS ratio, has significant impact on system performance. Most of existing studies aim to decide the ABS ratio to increase network throughput according to channel condition or traffic demands of user devices. However, none of them considers delay requirement of multimedia flows. Consequently, the paper proposes an adaptive configuration mechanism to dynamically change the ABS ratio to support quality of service (QoS) for multimedia communications by measuring the transmission capacity of each cell and the amount of urgent multimedia data. Through LTE-Sim experiments, we demonstrate that the proposed configuration mechanism can efficiently improve throughput of macrocells and picocells, while reducing video packet dropping due to expiration.

Long term evolution (LTE) is a standard widely used in cellular networks today. Both resource scheduling and pricing are two critical issues. However, existing studies address them separately, making the goals of improving system performance and increasing operator revenue conflicting. This paper proposes a pricing-aware resource scheduling (PARS) framework to conquer this conflict. It classifies users into three levels and has scheduling and pricing modules, which are installed in a base station and the core network of LTE, respectively. The scheduling module uses three-layer schedulers to assign resource to a flow by considering its packet delay, traffic amount, channel condition, and user level. The pricing module uses price elasticity of demand in economics to adaptively adjust the amount of money charged to users. Through experiments by LTE-Sim, we show that PARS achieves a good balance between performance and revenue, and provides quality of service for the flows with strict delay concerns.

Abstract In many applications and scenarios, sensors have to regularly report what they monitor from the environment and quickly notify the sink node of event occurrence in the sensing field. An in‐network data reduction technique, such as data aggregation and data compression, can help diminish the amount of data sent from sensors, which not only saves the network bandwidth but also preserves sensors' energy. However, such technique does not consider packet latency because of the aggregation or compression operation. When some sensors generate regular reports in lower data rates, their packets have to spend longer time to be aggregated or compressed, resulting in higher packet delays. Besides, when events occur, the network could suffer from instant congestion due to the generation of numerous event notifications. Motivated with the aforementioned observations, the paper develops a lightweight, latency‐aware routing for data compression (L2DC) scheme to reduce packet latency when applying the compression technique, to reduce the amount of data generated from sensors. L2DC gives event notifications a higher priority over regular reports and eliminates unnecessary notifications to avoid bursty network congestion. In addition, L2DC facilitates the data compression process by allowing each sensor to determine whether to keep packets for compression locally or to send them to a neighbor to be compressed in a distributed manner. Our L2DC scheme can be easily built on most ad hoc and sensor routing protocols because it provides auxiliary redundant packet elimination and relay node selection mechanisms to reduce packet latency. By using the ad hoc on‐demand distance vector protocol as the example, simulation results demonstrate the effectiveness of the L2DC scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Long term evolution (LTE) has become one of the dominating communication standards for 4G cellular systems. It employs the orthogonal frequency division multiple access (OFDMA) technique to enhance the efficiency of downlink transmissions, which divides the communication spectrum into multiple resource blocks. How to efficiently distribute these resource blocks among user equipments (UEs) is a challenging issue, which also significantly affects the system performance of LTE. Furthermore, in order to provide different quality of service (QoS) for various types of traffic flows, LTE defines two categories of traffic flows: guaranteed bit rate (GBR) and non-guaranteed bit rate (non-GBR). In the paper, we develop an efficient resource management mechanism to allocate downlink resource blocks to UEs so as to support QoS for GBR traffic flows while avoid starving non-GBR traffic flows. Our mechanism takes into account various network conditions such as channel quality, head-of-line (HOL) packet delay, QoS class identifier (QCI) value, and buffer length. Through the LTE-Sim network simulator, we demonstrate that the proposed mechanism can reduce both packet dropping and delay of GBR traffic flows. In addition, non-GBR traffic flows can still keep relatively high throughput to improve system performance.

In cellular networks, base station placement is a critical issue because it determines the construction cost and service quality. Existing placement approaches for 2G and 3G networks usually consider homogeneous base stations, where they have similar coverage areas and hardware features. Recently, the burgeoning LTE technology allows different types of base stations to collaboratively provide service in the same network. This characteristic motivates us to address the problem of placing heterogeneous base stations to serve user devices such that the overall cost is minimized while user demands are stratified. The problem is NP-hard, and thus we develop an efficient two-phase heuristic which first employs a geometric idea to provide coverage to all user devices and then adjusts the cell range to meet the power and bandwidth constraints of each base station. Experimental results show that our two-phase heuristic can reduce the construction cost and energy consumption of base stations.

In rotatable and directional (R&D) sensor networks, each sensor has sector-like sensing range and rotation ability. These sensors can support temporal coverage of objects by periodically rotating to cover them. We define that an object is δi-time covered if it is covered by a sensor for at least 0i≤ 1 portion of each period. Given a set of objects where they may have different δi-time covered demands, the paper proposes an R&D sensor deployment for heterogeneous objects (RSD-HO) problem which determines how to use the minimum number of R&D sensors to make every object be δi-time covered. The RSD-HO problem is NP-hard, so we propose an efficient heuristic by considering the distribution and δivalues of objects. Simulation results show that our RSD-HO heuristic requires fewer sensors compared with existing schemes. The paper contributes in defining an NP-hard RSD-HO problem and developing an efficient heuristic to solve it.

The paper considers a hybrid wireless sensor network with static and mobile sensors, where each static sensor can detect only one attribute of event while a mobile sensor can analyze multiple attributes of events. Static sensors monitor the environment and report where events appear. Mobile sensors then move to these event locations to conduct more in-depth analysis. A critical issue is how to schedule the traveling paths of mobile sensors so as to extend their lifetime. We formulate this issue as a multi-round multi-attribute sensor dispatch problem and prove it to be NP-complete. Then, we develop a two-phase dispatch heuristic that adopts the concepts of Pareto optimality and spanning-tree construction. Our heuristic allows arbitrary numbers of mobile sensors and event locations and tries to reduce and balance the energy consumption of mobile sensors in each round. Through simulations, we verify the effectiveness of our heuristic. The paper contributes in defining a new sensor dispatch problem and developing an energy-efficient solution to the problem.

DVB-H is developed to broadcast digital videos to handheld devices, but data loss is a concern due to the broadcast behavior. On the other hand, DVB-IPDC combines a wireless network with DVB-H to provide bidirectional communications. Such a wireless network can be used to handle data retransmissions and we call it a recovery network. The paper argues that network coding can improve retransmission efficiency of the recovery network since packet loss often exhibits high correlation. Besides, packets are heterogeneous in the sense of priority or importance. Thus, recovering different packets can obtain different benefits. Based on these two arguments, the paper proposes a maximum benefit problem, which asks the base station in the recovery network to use a limited number of coded packets for handheld devices to retrieve their lost packets such that the overall benefit is maximum. An efficient XOR coding scheme is developed to solve this problem. The paper contributes in addressing a new coding issue in DVB-IPDC.

ABSTRACT Constrained by the physical environments, thelong‐thintopology has recently been promoted for many practical deployments of wireless sensor networks (WSNs). In general, a long‐thin topology is composed of a number of long branches of sensor nodes, where along a branch each sensor node has only one potential parent node toward the sink node. Although data aggregation may alleviate excessive packet contention, the maximum payload size of a packet and the dynamically changing traffic loads may severely affect the amount of sensor readings that may be collected along a long branch of sensor nodes. In addition, many practical applications of long‐thin WSNs demand the exact sensor readings at each location along the deployment areas for monitoring and analysis purposes, so sensor readings may not be aggregated when they are collected. This paper proposes a lightweight, self‐adaptive scheme that designates multiple collection nodes, termedlock gates, along a long‐thin network to collect sensor readings sent from their respective upstream sensor nodes. The self‐adaptive lock gate designation scheme balances between the responsiveness and the congestion of data collection while mitigating the funneling effect. The scheme also dynamically adapts the designation of lock gates to accommodate the time‐varying sensor reading generation rates of different sensor nodes. A testbed of 100 Jennic sensor nodes is developed to demonstrate the effectiveness of the proposed lock gate designation scheme. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Recently, digital video broadcasting‐handheld (DVB‐H) and IP datacast over DVB‐H (DVB‐IPDC) have been developed to support broadcasting services. DVB‐H is designed to support digital video broadcast for handheld devices, whereas DVB‐IPDC can integrate with an IP‐relay network to complement the data loss problem in DVB‐H. Assuming that WiMAX networks are adopted to support DVB‐IPDC, this paper points out two critical problems: group packet loss (GPL) and broadcast data handover (BDH). GPL occurs when there is a burst of retransmission requests for the same pieces of data with high spatial or temporal correlation. BDH happens when some devices that made the above requests handover to new serving cells. To solve these problems, we propose lazy wait and group acknowledgement schemes to alleviate duplicate requests by exploiting their spatial and temporal correlations. This not only reduces the requests submitted by neighboring devices in both space and time domains but also avoids handovering devices from sending duplicate requests in new cells. Through mathematical analysis, we show how to adaptively adjust the timers of lazy wait and group acknowledgement based on channel quality. Simulation results prove that our schemes can efficiently reduce retransmission requests and retransmission packets, thus alleviating congestion in the IP‐relay network. Copyright © 2011 John Wiley & Sons, Ltd.

Due to hardware design or cost consideration, sensors may possess sector-like sensing coverage. Furthermore, by stepper motors, sensors can rotate to cover the objects around them. This type of sensors are called rotatable and directional (R&D) sensors. Through rotation, R&D sensors provide temporal coverage to objects by "periodically” detecting their existence. In the paper, we first develop an event-driven surveillance system by R&D sensors, where objects are monitored by the sensors equipped with infrared detectors and cameras. When an object is taken away, the sensor monitoring the object reports a warning message along with detailed snapshots from the surroundings. Then, motivated by the system, we formulate an R&D sensor deployment problem, which tries to deploy the minimum number of R&D sensors to cover a given set of objects such that each object is covered by 0 < \delta \le 1 ratio of time in every frame. We show this problem to be NP-hard and propose two efficient heuristics. The maximum covering deployment (MCD) heuristic iteratively deploys a sensor to cover more objects, and performs well when objects congregate together. The disk-overlapping deployment (DOD) heuristic deploys sensors to cover the joint sectors of overlapped disks, so it works better when objects are arbitrarily placed in the sensing field. The paper contributes in defining a new temporal coverage model by R&D sensors, developing a surveillance application for this model, and proposing efficient heuristics to reduce the deployment cost.

Abstract Wireless sensor networks (WSNs) offer a convenient way to monitor physical environments. In the past, WSNs are all considered static to continuously collect information from the environment. Today, by introducing intentional mobility to WSNs, we can further improve the network capability on many aspects, such as automatic node deployment, flexible topology adjustment, and rapid event reaction. In this paper, we survey recent progress in mobile WSNs and compare works in this field in terms of their models and mobility management methodologies. The discussion includes three aspects. Firstly, we discuss mobility management of mobile sensors for the purposes of forming a better WSN, enhancing network coverage and connectivity, and relocating some sensors. Secondly, we introduce path‐planning methods for data ferries to relay data between isolated sensors and to extend a WSN's lifetime. Finally, we review some existing platforms and discuss several interesting applications of mobile WSNs. Copyright © 2010 John Wiley & Sons, Ltd.

By introducing the relay capability, the IEEE 802.16j standard is developed to improve the WiMAX performance. Under the transparent mode, existing studies aim at improving network throughput by increasing the transmission rates of mobile stations (MSs). However, we show that using higher rates will let MSs consume more energy. In the paper, we define an energy-conserved uplink resource allocation (EURA) problem in 802.16j networks under the transparent mode, which asks how to arrange the uplink resource to 1) satisfy MSs' requests and 2) minimize their energy consumption. Objective 1 is necessary while objective 2 should be achieved when objective 1 is met. The above bi-objective problem is especially important when the network is non-saturated. The EURA problem is NP-hard and we propose a heuristic with two key designs. First, we exploit relay stations to allow more concurrent uplink transmissions to fully use the frame space. Second, we reduce MSs' transmission powers by adjusting their rates and paths. Simulation results show that our heuristic can save up to 80% of MSs' energy as compared with existing work.

You-Chiun Wang, Tomoaki Ohtsuki, Athanasios (Thanos) Vasilakos, Ashutosh Sabharwal, Yuh-Shyan Chen, Yu-Chee Tseng; Algorithms, Protocols and Future Applica

We consider a hybrid wireless sensor network with static and mobile nodes. Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. A big challenge is how to schedule these mobile sensors' traveling paths in an energy-balanced way so that their overall lifetime is maximized. We formulate this problem as a multiround sensor dispatch problem and show it to be NP-complete. Then, we propose a centralized and a distributed heuristics to schedule mobile sensors' traveling paths. Our heuristics allow arbitrary numbers of mobile sensors and event locations in each round and have an energy-balanced concept in mind. The centralized heuristic tries to minimize mobile sensors' moving energy while keeping their energy consumption balanced. The distributed heuristic utilizes a grid structure for event locations to bid for mobile sensors. Through simulations, we show the effectiveness of our schemes. This paper contributes in defining a more general multiround sensor dispatch problem and proposing energy-efficient solutions to it.

The DVB-H standard has been defined to provide digital video broadcast to mobile handheld devices. However, without a request channel, a handheld device may encounter data loss and thus it requires an additional recovery architecture to relieve this problem. In this paper, we follow the DVB-IPDC standard and propose combining a DVB-H system with an IP- relay wireless network. A handheld device losing DVB-H data can thus request the relay network for retransmission. Under this architecture, we identify two critical issues, group packet loss (GPL) and broadcast data handover (BDH). The GPL problem occurs when there are bursty requests for retransmissions of the same data with high spatial and temporal locality, causing network congestion. The BDH problem occurs when these requesting devices move out of their current serving cells. In this paper, we propose a bulk request recovery (BRR) scheme to solve these problems by exploiting spatial and temporal locality of recovery requests. Our scheme can efficiently reduce duplicate requests and schedule retransmissions of lost packets, so both GPL and BDH problems are relieved. Simulations results are also presented to verify the effectiveness of our result.

WiFi networks have been deployed in many regions such as buildings and campuses to provide wireless Internet access. However, to support ubiquitous wireless service, one possibility is to integrate these narrow-range WiFi networks with a wide-range network such as WiMAX. Under this WiMAX-WiFi integrated network, how to conduct energy-efficient handovers is a critical issue. In this paper, we propose a handover scheme with geographic mobility awareness (HGMA) by considering the past handover patterns of mobile devices. HGMA can conserve the energy of handovering devices from three aspects. First, it prevents mobile devices from triggering unnecessary handovers by measuring their received signal strength and moving speeds. Second, it includes a handover candidate selection (HCS) method for mobile devices to intelligently select a subset ofWiFi access points or WiMAX relay stations to be scanned. Therefore, mobile devices can reduce their network scanning and thus save their energy. Third, HGMA prefers mobile devices staying in their original WiMAX or WiFi networks. This can prevent devices from consuming too much energy on interface switching. Simulation results show that HGMA can reduce about 69% and 30% of energy consumption on network scanning and interface switching, respectively, and with 16% to 64% more probabilities for mobile devices staying in WiFi networks.

In many WSN (wireless sensor network) applications, such as [1], [2], [3], the targets are to provide long-term monitoring of environments. In such applications, energy is a primary concern because sensor nodes have to regularly report data to the sink and need to continuously work for a very long time so that users may periodically request a rough overview of the monitored environment. On the other hand, users may occasionally query more in-depth data of certain areas to analyze abnormal events. These requirements motivate us to propose a multiresolution compression and query (MRCQ) framework to support in-network data compression and data storage in WSNs from both space and time domains. Our MRCQ framework can organize sensor nodes hierarchically and establish multiresolution summaries of sensing data inside the network, through spatial and temporal compressions. In the space domain, only lower resolution summaries are sent to the sink; the other higher resolution summaries are stored in the network and can be obtained via queries. In the time domain, historical data stored in sensor nodes exhibit a finer resolution for more recent data, and a coarser resolution for older data. Our methods consider the hardware limitations of sensor nodes. So, the result is expected to save sensors' energy significantly, and thus, can support long-term monitoring WSN applications. A prototyping system is developed to verify its feasibility. Simulation results also show the efficiency of MRCQ compared to existing work.

Wireless sensor networks provide a convenient manner to monitor the physical environments. How to extend the network lifetime by reducing the amount of message transmissions is a critical issue. In this paper, we propose a multiresolution compression and storage (MCS) framework to compress and preserve sensing data in a wireless sensor network. Our MCS framework adopts spatial and temporal compression schemes to reduce the amount of message transmissions, so the network lifetime can be prolonged and the network congestion can be alleviated. In addition, we also develop a storage mechanism to maintain sensing data in sensor nodes, so that users can query more detailed data when necessary. Our proposed methods consider the hardware limitations of sensor nodes. We also implement a prototyping system on the MICAz Mote platform.

Sensor deployment is a critical issue because it affects the cost and detection capability of a wireless sensor network. In this work, we consider two related deployment problems: sensor placement and sensor dispatch. The former asks how to place the least number of sensors in a field to achieve sensing coverage and network connectivity, and the latter asks how to determine from a set of mobile sensors a subset of sensors to be moved to an area of interest with certain objective functions such that the coverage and connectivity properties are satisfied. This work is targeted toward planned deployment. Our solution to the placement problem allows an arbitrary-shaped sensing field possibly with arbitrary-shaped obstacles and an arbitrary relationship between the communication distance and sensing distance of sensors and, thus, significantly relaxes the limitations of existing results. Our solutions to the dispatch problem include a centralized one and a distributed one. The centralized one is based on adopting the former placement results and converting the problem to the maximum-weight maximum-matching problem with the objective of minimizing the total energy consumption to move sensors or maximizing the average remaining energy of sensors after movement. Designed in a similar way, the distributed one allows sensors to determine their moving directions in an autonomous manner.

One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.

In this paper, a hybrid sensor network consisting of static and mobile sensors is considered, where static sensors are used to detect events, and mobile sensors can move to event locations to conduct more advanced analysis. By exploring the load balance concept, we propose a CentralSD algorithm to efficiently dispatch mobile sensors. Our algorithm is general in that the numbers of mobile sensors and events can be arbitrary. When mobile sensors are more than event locations, we transform the dispatch problem to a maximum-matching problem in a weighted bipartite graph. When there are fewer mobile sensors than event locations, we propose an efficient clustering scheme to group event locations so that the maximum-matching approach can still be applied. To reduce message cost, we also develop a distributed GridSD algorithm. Simulation results are presented to verify the effectiveness of the proposed algorithms.

Wireless sensor networks (WSN) provide an inexpensive and convenient way to monitor physical environments. Integrating the context-aware capability of WSN into surveillance systems is an attractive direction. We thus propose an integrated mobile srveillance and wireless sensor (iMouse) system, which consists of a large number of inexpensive static sensors and a small number of more expensive mobile sensors. The former is to monitor the environment, while the latter can move to certain locations and takes more advanced actions. The iMouse system is a mobile, context-aware surveillance system. We demonstrate our current prototyping for home security applications. Besides, we analyze its event detection delay under an any-sensor-detection model.

Abstract One critical issue in multihop ad‐hoc networks is the medium access control (MAC). The IEEE 802.11 MAC protocol is originally designed for fully connected, one‐hop ad‐hoc networks but not for multihop ad‐hoc networks. In addition to the well known hidden‐terminal problem, we found that IEEE 802.11 also suffers from an erroneous reservation problem which occurs when RTS‐CTS exchange fails but the channel is incorrectly reserved. In this paper, we propose a jamming‐based MAC (JMAC) protocol that is not only free from both the hidden‐terminal and the erroneous reservation problems but also allows more concurrent transmission/receipt activities for stations within each other's transmission range. The idea behind the JMAC is to separate source stations' traffic from destination stations' traffic into different channels (i.e. dividing the shared medium into two channels), and explicitly signal the channel status by jamming the channels. Simulation results show that although the channel division incurs some cost, the advantages of being free from the erroneous reservation and the hidden‐terminal problems, and the benefits of more concurrent transmissions will compensate the cost and provide higher channel utilization when data frame size is median or large. Copyright © 2004 John Wiley & Sons, Ltd.