Abstract — In this paper, we model and characterize the performance of multihop radio networks in the presence of energy constraints, and design routing algorithms to optimally utilize the available energy. The energy model allows vastly different energy sources in heterogeneous environments. The proposed algorithm is shown to achieve a competitive ratio (i.e., the ratio of the performance of any off-line algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) that is asymptotically optimal with respect to the number of nodes in the network. The algorithm assumes no statistical information on packet arrivals and can easily be incorporated into existing routing frameworks (e.g., proactive or on-demand methodologies) in a distributed fashion. Simulation results confirm that the algorithm performs very well in terms of maximizing the throughput of an energy-constrained network. Further, a new thresholdbased scheme is proposed to reduce the routing overhead while incurring only minimum performance degradation.

An important performance consideration for wireless sensor networks is the amount of information collected by all the nodes in the network over the course of network lifetime. Since the objective of maximizing the sum of rates of all the nodes in the network can lead to a severe bias in rate allocation among the nodes, we advocate the use of lexicographical max-min (LMM) rate allocation for the nodes. To calculate the LMM rate allocation vector, we develop a polynomial-time algorithm by exploiting the parametric analysis (PA) technique from linear programming (LP), which we call serial LP with Parametric Analysis (SLP-PA). We show that the SLP-PA can be also employed to address the so-called LMM node lifetime problem much more efficiently than an existing technique proposed in the literature. More important, we show that there exists an elegant duality relationship between the LMM rate allocation problem and the LMM node lifetime problem. Therefore, it is sufficient to solve any one of the two problems and important insights can be obtained by inferring duality results for the other problem.

In this paper, we analyze the impact of straight line routing in large homogeneous multi-hop wireless networks. We estimate the nodal load, which is defined as the number of packets served at a node, induced by straight line routing. For a given total offered load on the network, our analysis shows that the nodal load at each node is a function of the node’s Voronoi cell, the node’s location in the network, and the traffic pattern specified by the source and destination randomness and straight line routing. The traffic pattern determines where the hot spot is created in the network, and straight line routing itself can balance the relay load in certain cases. In the asymptotic regime, each node’s probability that the node serves a packet arriving to the network can be approximated as the multiplication of a half length of its Voronoi cell perimeter and the probability density function that a packet goes through the node’s location. Both simulations and analysis confirm that this approximation converges to the exact value. The scaling order of network performance in our analysis is independent of traffic patterns generated by source-destination pair randomness, but for a given node the performance of each node is strongly related to the source-destination pair randomness. ulations

Abstract—In this paper, we investigate the use of proactive multipath routing to achieve energy-efficient operation of ad hoc wireless networks. The focus is on optimizing tradeoffs between the energy cost of spreading traffic and the improved spatial balance of energy burdens. We propose a simple scheme for multipath routing based on spatial relationships among nodes. Then, combining stochastic geometric and queueing models, we develop a continuum model for such networks, permitting an evaluation of different types of scenarios, i.e., with and without energy replenishing and storage capabilities. We propose a parameterized family of energy balancing strategies and study the spatial distributions of energy burdens based on their associated second-order statistics. Our analysis and simulations show the fundamental importance of the tradeoff explored in this paper, and how its optimization depends on the relative values of the energy reserves/storage, replenishing rates, and network load characteristics. For example, one of our results shows that the degree of spreading should roughly scale as the square root of the bits meters load offered by a session. Simulation results confirm that proactive multipath routing decreases the probability of energy depletion by orders of magnitude versus that of a shortest path routing scheme when the initial energy reserve is high. Index Terms—Gaussian random field, I queue, sensor networks, shot-noise process, stochastic geometry. I.

Abstract — Recent research has shown that interference can make a significant impact on the performance of multihop wireless networks. Researchers have studied interference-aware topology control recently [1]. In this paper, we study routing problems in a multihop wireless network using directional antennas with dynamic traffic. We present new definitions of link and path interference that are suitable for designing better routing algorithms. We then formulate and optimally solve two power constrained minimum interference single path routing problems. Routing along paths found by our interference-aware algorithms tends to have less channel collisions and higher network throughput. Our simulation results show that, compared with the minimum power path routing algorithm, our algorithms can reduce average path interference by 40 % or more at the cost of a minor power increase. We also extend our work towards survivable routing by formulating and solving the power constrained minimum interference node-disjoint path routing problem.

This paper reviews some of the recent advances in the development of algorithms for wireless sensor networks. We focus on sensor deployment and coverage, routing and sensor fusion.

Abstract — Energy efficiency is an important issue in multihop wireless networks with energy concerns. Usually it is achieved with accurate knowledge of the traffic pattern and/or the current network information such as the remaining energy level. We investigate the problem of designing a routing scheme to minimize the maximum energy utilization of a multihop wireless network with weak assumption of the traffic pattern and without ongoing collection of network information. We develop polynomial size LP models to design such a routing scheme. We discuss generalizations of the LP models to various radio transmission models. In an interference-limited scenario, we show how to guarantee schedulability of the oblivious routing. We present an extension to consider lossy links. We also discuss implementation issues. The LP models achieve performance close to what an oracle can achieve in the performance study. The results for multihop wireless networks with a single sink are especially good. We make a first stride in designing a traffic-oblivious energy-aware routing framework in multihop wireless networks. I.

Abstract — Sensor network nodes are often limited in battery capacity and processing power. Thus, it is imperative to develop solutions that are both energy and computationally efficient. In this work, we present a simple static multi-path routing approach that is optimal in the large system limit. In a network with energy replenishment, the largeness comes into play because the energy claimed by each packet is small compared to the battery capacity. Compared to the other routing algorithms in the literature, this static routing scheme exploits the knowledge on the patterns of traffic and energy replenishment, and does not need to collect instantaneous information on node energy. We also outline possible approaches for a distributed computation of the optimal policy, and propose heuristics to build the set of pre-computed paths. The simulations verify that the static scheme outperforms leading dynamic routing algorithms in the literature, and is close to optimal when the energy claimed by each packet is relatively small compared to the battery capacity.

Abstract — In this work, we study the problem of minimizing the total power consumption in a multi-hop wireless network subject to a given offered load. It is well-known that the total power consumption of multi-hop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal crosslayer solution to this problem is centralized, and with high computational complexity. In this paper, we develop a lowcomplexity and distributed algorithm that is provably powerefficient. In particular, under the node exclusive interference model, we can show that the total power consumption of our algorithm is at most twice as large as the power consumption of the optimal (but centralized and complex) algorithm. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another sub-optimal centralized algorithm in the literature.

Some of the first routing algorithms for geographically aware wireless networks used the Delaunay triangulation among the network's nodes as the underlying connectivity graph [4]. These solutions were considered impractical, however, because in general the Delaunay triangulation may contain arbitrarily long edges, and because calculating the Delaunay triangulation generally requires a global view of the network. Many other algorithms were then suggested for geometric routing, often assuming random placement of network nodes for analysis or simulation [30, 5, 31, 16]. We show that, when the nodes are uniformly placed in the unit disk, the Delaunay triangulation does not contain long edges, it is easy to compute locally and it is in many ways optimal for geometric routing and flooding. In particular, we prove that, with high probability, the # Work by M.S. has been supported by a grant from the U.S.-Israeli Binational Science Foundation, by a grant from the Israel Science Fund (for a Center of Excellence in Geometric Computing), by NSF Grants CCR-9732101, CCR-00-98246, and by the Hermann Minkowski-- MINERVA Center for Geometry at Tel Aviv University.