Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this issue by adopting mobility and space-division multiple access (SDMA) technique. Specifically, mobile collectors, called SenCars in this paper, work like mobile base stations and collect data from associated sensors via single-hop transmissions so as to achieve uniform energy consumption. We also apply SDMA technique to data gathering by equipping each SenCar with multiple antennas such that distinct compatible sensors may successfully make concurrent data uploading to a SenCar. To investigate the utility of the joint design of controlled mobility and SDMA technique, we consider two cases, where a single SenCar and multiple SenCars are deployed in a WSN, respectively. For the single SenCar case, we aim to minimize the total data gathering time, which consists of the moving time of the SenCar and the data uploading time of sensors, by exploring the trade-off between the shortest moving tour and the full utilization of SDMA. We refer to this problem as mobile data gathering with SDMA, or MDG-SDMA for short. We formalize it into an integer linear program (ILP) and propose three heuristic algorithms for it. In the multi-SenCar case, the sensing field is divided into several regions, each having a SenCar. We focus on minimizing the maximum data gathering time among different regions and refer to it as mobile data gathering with multiple SenCars and SDMA (MDG-MS) problem. Accordingly, we propose a region-division and tour-planning (RDTP) algorithm in which data gathering time is balanced among different regions. We carry out extensive simulations and the results demonstrate that our proposed algorithms significantly outperform single SenCar and non-SDMA schemes.

Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this important issue in WSNs by adopting mobility and space-division multiple access (SDMA) technique to optimize system performance. Specifically, a mobile data collector, for convenience, called SenCar in this paper, is deployed in a WSN. It works like a mobile base station and polls each sensor while traversing its transmission range. Each sensor directly sends data to the SenCar without any relay so that the lifetime of sensors can be prolonged. We also consider applying SDMA technique to data gathering by equipping the SenCar with two antennas. With SDMA, two distinct compatible sensors may successfully make concurrent data uploading to the SenCar. Intuitively, if the SenCar can always simultaneously communicate with two compatible sensors, data uploading time can be cut into half in the ideal case. We focus on the problem of minimizing the total time of a data gathering tour which consists of two parts: data uploading time and moving time. To better enjoy the benefit of SDMA, the SenCar may have to visit some specific locations where more sensors are compatible, which may adversely prolong the moving path. Hence, an optimum solution should be a tradeoff between the shortest moving path and full utilization of SDMA. We refer to this optimization problem as mobile data gathering problem with SDMA, or MDG-SDMA for short. We formalize the MDG-SDMA problem into an integer program (IP) and then propose three heuristic algorithms that provide practically good solutions to the problem. Our simulation results demonstrate that the proposed algorithms can greatly reduce the total data gathering time compared to the non-SDMA algorithm with only minimum overhead.

In this paper, we propose a new data gathering mechanism for large scale wireless sensor networks by introducing mobility into the network. A mobile data collector, or for convenience called M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station and gathers data while moving through the field. An M-collector starts the data gathering tour periodically from the static data sink, polls each sensor while traversing the transmission range of the sensor, then collects data directly from the sensor without relay (i.e., in a single hop), finally returns and uploads data to the data sink. Since data packets are gathered directly without relay and collision, the lifetime of sensors is expected to be prolonged, and sensors can be made very simple and inexpensive. We focus on the problem of minimizing the length of each data gathering tour and refer to this problem as the single-hop data gathering problem, or SHDGP for short. We first formalize the SHDGP problem into a mixed integer program and prove its NP-hardness. We then present a heuristic tour-planning algorithm for a single M-collector. For the applications with a strict distance/time constraint for each data gathering tour, we also utilize multiple M-collectors to traverse through several shorter sub-tours concurrently to satisfy the distance/time constraint. Our single-hop data gathering scheme will improve the scalability and solve intrinsic problems of large scale homogeneous networks, and can be used in both connected networks and disconnected networks. The simulation results demonstrate that the new data gathering algorithm can greatly reduce the moving distance of the collectors compared to the covering line approximation algorithm, and is close to the optimal algorithm in small networks. In addition, the proposed data gathering scheme can prolong the network lifetime significantly compared to a network which has only a static data collector, or a network in which the mobile collector can only move along straight lines.

In this paper, we study two-layered heterogeneous sensor networks where two types of nodes are deployed: the basic sensor nodes and the cluster head nodes. The basic sensor nodes are simple and have limited power supplies, whereas the cluster head nodes are much more powerful and have many more power supplies, which organize sensors around them into clusters. Such two-layered heterogeneous sensor networks have better scalability and lower overall cost than homogeneous sensor networks. We propose using polling to collect data from sensors to the cluster head since polling can prolong network life by avoiding collisions and reducing the idle listening time of sensors. We focus on finding energy-efficient and collision-free polling schedules in a multihop cluster. To reduce energy consumption in idle listening, a schedule is optimal if it uses the minimum time. We show that the problem of finding an optimal schedule is NP-hard and then give a fast online algorithm to solve it approximately. We also consider dividing a cluster into sectors and using multiple nonoverlapping frequency channels to further reduce the idle listening time of sensors. We conducted simulations on the NS-2 simulator and the results show that our polling scheme can reduce the active time of sensors by a significant amount while sustaining 100 percent throughput.

In this paper, we present a novel contention-based medium access control (MAC) protocol, namely, the Channel Reservation MAC (CR-MAC) protocol. The CR-MAC protocol takes advantage of the overhearing feature of the shared wireless channel to exchange channel reservation information with little extra overhead. Each node can reserve the channel for the next packet waiting in the transmission queue during the current transmission. We theoretically prove that the CR-MAC protocol achieves much higher throughput than the IEEE 802.11 RTS/CTS mode under saturated traffic. The protocol also reduces packet collision, thereby saving the energy for retransmission. We evaluate the protocol by simulations under both saturated traffic and unsaturated traffic. Our simulation results not only validate the theoretical analysis on saturated throughput, but also reveal other good features of the protocol. For example, under saturated traffic, both the saturated throughput and fairness measures of the CR-MAC are very close to the theoretical upper bounds. Moreover, under unsaturated traffic, the protocol also achieves higher throughput and better fairness than IEEE 802.11 RTS/CTS.

Opportunistic medium access (i.e., multiuser diversity) and MIMO techniques (i.e., multiple-antenna techniques) are two effective ways to achieve a substantial throughput gain in a multiuser wireless system. In this paper, we propose a medium access control (MAC) protocol with opportunistic medium access and multiuser MIMO techniques (MAC-OMA/MM) in Multi-channel Multi-radioWireless Local Area Networks (WLANs) to explore the utility of the joint design of these two techniques for the challenging MAC design. Specially, in addition to utilizing multiple channels simultaneously and multiple radio transceivers dynamically, multiuser spatial multiplexing and multiuser diversity are employed in each frequency channel to improve system performance. The key ideas of MAC-OMA/MM can be summarized as follows. By utilizing ATIM (Ad-hoc Traffic Indication Message) windows as in IEEE 802.11 power saving mechanism (PSM) under the distributed coordinate function (DCF) mode, user selection and channel negotiation are conducted between the AP and users via ATIM messages on a common channel. Multiuser diversity are employed to opportunistically schedule among multiple candidate users to optimize data transmission. During data exchange, on each frequency channel, the AP can transmit data to two distinct users simultaneously in the downlink with the help of multiuser spatial multiplexing, and two users can concurrently send data to the AP by uplinkdownlink duality in the uplink, which creates an extra dimension in spatial domain to further leverage the effect of multiuser diversity and multi-channel gains. Another contribution of this paper is to provide an analytical model to characterize the impact of our protocol on the system throughput and energy efficiency performance. Extensive simulations have been conducted and the results demonstrate that our protocol outperforms existing multi-channel MAC protocols with only minimal additional overhead and minor enhancements to the IEEE 802.11 PSM.

Recent years have witnessed a surge of the interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this essential issue in WSNs by introducing the mobility and MIMO capability to the data sink in order to optimize the system performance. Specifically, the mobile sink moves along a pre-determined path and collects data packets at some polling stations. With a proper planning on the moving path, we can reduce the data relaying to the minimum, which effectively improves the energy efficiency and also circumvents the non-uniformity of energy consumption among sensor nodes. We also apply MIMO technique to the mobile sink by mounting multiple antennas on it, which provides a radical way to improve the system capacity. With the support of MIMO technique, the mobile sink can simultaneously collect data from multiple compatible sensors. Finally, we theoretically analyze the performance of mobile data gathering in a WSN enhanced by MIMO support, and also investigate its efficiency by simulation experiments. The results demonstrate that the proposed mechanism can greatly improve the system throughput and energy efficiency with minimum additional overhead.

In this paper, a contention-free polling protocol and a load-balancing algorithm, for two-layered heterogeneous sensor networks were proposed, based on the energy consumption features of the physical layer, MAC layer and network layer. By introducing some powerful nodes, sensors can be organized into clusters. Each powerful node works as a local data sink and gathers data from sensors in the cluster. The power consumption model of IEEE 802.15.4-based transceivers was first analyzed. Based on physical layer features of such low-power, low-rate transceivers, a contention-free polling protocol for inner-cluster data gathering was designed, so that packet collision can be avoided. By deactivating the transceivers of sensors which do not need to transmit or receive packets, energy consumption on idle listening can be saved. A load-balancing algorithm at network layer was also proposed to maximize the network lifetime by taking into consideration of power consumption features at the physical and MAC layers. Simulation results show that the proposed scheme can achieve more than one order of magnitude improvement compared to the FLOW-CSMA combination and reduce the duration of sensor duty cycle by at least 50% under various offered traffic loads.

Wireless MIMO (multiple input multiple output) mesh networks recently emerge as a flexible, low-cost and multi-functional networking platform with wired infrastructure connected to the Internet. Mesh routers are equipped with multiple radio transceivers that can work simultaneously. This MIMO feature greatly improves data throughput of mesh routers. A critical issue in MIMO mesh networks is to maintain network coverage and routing for a long lifetime with high energy efficiency. As more and more outdoor applications require long-lasting, high energy efficient and continuously-working mesh networks with battery-powered mesh routers, it is important to maximize the performance of mesh networks from a battery aware point of view. Recent study in battery technology reveals that discharging of a battery is nonlinear. Batteries tend to discharge more power than needed, and reimburse the over-discharged power later if they have sufficiently long recovery time. To take advantage of the battery recovery property, in this paper we first study the relationships between various MIMO transceiver parameters and their battery parameters to give an energy model for MIMO transceivers. We then present a multiple current battery model that can accurately describe battery behaviors with multiple current inputs. Based on these two models, we propose a battery aware MIMO mesh network power scheduling scheme. The scheme consists of two algorithms: the coverage algorithm and the backhaul routing algorithm. The key idea of the coverage algorithm is to let neighboring mesh routers collaboratively adjust their transceiver radii to dynamically recover their over-discharged battery power. The backhaul routing algorithm adopts the multiple current battery model to calculate battery discharging loss at routers for scheduling mesh backhaul routing. We conducted simulations to evaluate the performance of the proposed scheme. The results show that network lifetime can be improved by up to 10.3% and 16.1% for homogeneous and heterogeneous mesh networks, respectively.

In this paper, we present a novel sensor deployment algorithm, called the adaptive triangular deployment (ATRI) algorithm, for large-scale unattended mobile sensor networks. The ATRI algorithm aims at maximizing coverage area and minimizing coverage gaps and overlaps by adjusting the deployment layout of nodes close to equilateral triangulation, which is proven to be the optimal layout to provide the maximum no-gap coverage. The algorithm only needs the location information of nearby nodes, thereby avoiding communication cost for exchanging global information. By dividing the transmission range into six sectors, each node adjusts the relative distance to its one-hop neighbors in each sector separately. The distance threshold strategy and the movement state diagram strategy are adopted to avoid the oscillation of nodes. The simulation results show that the ATRI algorithm achieves a much larger coverage area and smaller average moving distance of nodes than existing algorithms. We also show that the ATRI algorithm is applicable to practical environments and tasks such as working in both bounded and unbounded areas and avoiding irregularly shaped obstacles. In addition, the density of nodes can be adjusted adaptively to different requirements of tasks.

In this paper, we propose a new data gathering mechanism for large-scale multihop sensor networks. A mobile data observer, called SenCar, which could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station in the network. SenCar starts the data gathering tour periodically from the static data processing center, traverses the entire sensor network, gathers the data from sensors while moving, returns to the starting point, and, finally, uploads data to the data processing center. Unlike SenCar, sensors in the network are static and can be made very simple and inexpensive. They upload sensed data to SenCar when SenCar moves close to them. Since sensors can only communicate with others within a very limited range, packets from some sensors may need multihop relays to reach SenCar. We first show that the moving path of SenCar can greatly affect network lifetime. We then present heuristic algorithms for planning the moving path/circle of SenCar and balancing traffic load in the network. We show that, by driving SenCar along a better path and balancing the traffic load from sensors to SenCar, network lifetime can be prolonged significantly. Our moving planning algorithm can be used in both connected networks and disconnected networks. In addition, SenCar can avoid obstacles while moving. Our simulation results demonstrate that the proposed data gathering mechanism can prolong network lifetime significantly compared to a network that has only a static observer or a network in which the mobile observer can only move along straight lines.

In this paper, we consider the problem of positioning mobile cluster heads and balancing traffic load in a hybrid sensor network, which consists of two types of nodes: basic static sensor nodes and mobile cluster heads. In such a network, sensor nodes are organized into clusters and form the lower layer of the network. At the higher layer, cluster heads collect sensing data from sensors and forward data to outside observers. Such two-layer hybrid networks are more scalable and energy-efficient than homogeneous sensor networks. We show that the locations of cluster head-s can affect network lifetime significantly. The problem of maximizing network lifetime through dynamically positioning cluster heads in the network (referred to as the CHL problem in this paper) turns out to be NP-hard. We present a heuristic algorithm for positioning cluster heads and balancing traffic load in the network. We show that by moving the cluster head to a better location, the traffic load can be balanced and network lifetime can be prolonged. We conducted simulations on the NS-2 simulator, and the result-s show that our clustering algorithm can increase network lifetime by up to 35% after only three rounds of adjustments, compared to the optimal lifetime of the initial network layout.

In this paper, we present a novel contention-based medium access control (MAC) protocol, namely, the channel reservation MAC (CR-MAC) protocol. The CR-MAC protocol takes advantage of the overhearing feature of the shared wireless channel to exchange the channel reservation information with little extra overhead. Each node can reserve the channel for the next packet waiting in the transmission queue during the current transmission. We theoretically prove that the CR-MAC protocol achieves much higher throughput than the IEEE 802.11 RTS/CTS mode under saturated traffic. The protocol also reduces the packet collision, thereby saving the energy for retransmission as well. We also evaluate the protocol by simulations under both saturated traffic and unsaturated traffic. Our simulation results not only validate the theoretical analyses on saturated throughput, but also reveal some other good features of the protocol. For example, under saturated traffic, both the saturated throughput and fairness measures of the CR-MAC are very close to the theoretical upper bounds. Moreover, under unsaturated traffic, the protocol also achieves higher throughput and better fairness than IEEE 802.11 RTS/CTS.

In this paper we propose a multi-channel polling algorithm in multi-hop clusters of hybrid sensor networks. The hybrid sensor network consists of two types of nodes: basic sensor nodes and cluster head nodes. Basic sensor nodes have limited communication capacity and mainly focus on sensing the environment, while cluster head nodes are equipped with more powerful transceivers but simpler sensing modules. The cluster head node organizes basic sensor nodes around it into a cluster, and collects sensing data from sensors and forwards data to the outsider observer. This type of network has better energy-efficiency since traffic within cluster is scheduled by cluster head and packet collisions can be avoided. In addition, idle listening time can be shortened by turning off the transceivers of sensor nodes after data has been forwarded to cluster head. We focus on finding energy efficient and collision-free polling schedules in the multi-hop cluster with multiple frequency channels. Due to its energy efficiency and scalability, the proposed algorithm is very suitable for applications such as large scale environment monitoring. We also implement the proposed algorithm on NS-2. Simulation results show that multi-channel polling algorithm shortens the active time of sensor nodes by a significant amount compared to single channel polling. In the case that the total frequency bandwidth allocated to the cluster is fixed, the optimal number of channels can be obtained.

In this paper, we present a new position-based routing algorithm for mobile ad hoc networks. The proposed algorithm minimizes the effect of inaccurate location information on routing, which is caused by periodical updates of the node location information in the network. The algorithm achieves low communication complexity of O(/spl radic/n), compared to other existing position-based algorithms with O(n) complexity, where n is the number of nodes in the network. In addition, unlike some existing routing algorithms, the new algorithm is insensitive to the mobility of mobile nodes and consistently performs well for various mobilities.

A key challenge in wireless sensor networks is to achieve maximal network lifetime with dynamic power management on sensor nodes. In this paper we investigate the node level power control scheduling on densely deployed sensor networks. We prove that given homogeneous Poisson sensing traffic in a sensor network, the routing traffic is heterogeneous. We then introduce a well defined power control model to adapt to heterogeneous traffic, and propose a data-centric energy efficient scheduling protocol under the power model. The simulation results show that our protocol can reduce about 61.4% of power consumption at the sensor node level and achieve 33.3% to 43.5% energy efficiency at the network level, compared to other existing protocols.