Abstract-Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance.
Abstract-Controlling the mobility pattern of mobile nodes (e.g., robots) to monitor a given field is a well-studied problem in sensor networks. In this setup, absolute control over the nodes' mobility is assumed. Apart from the physical ones, no other constraints are imposed on planning mobility of these nodes. In this paper, we address a more general version of the problem. Specifically, we consider a setting in which mobility of each node is externally constrained by a schedule consisting of a list of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged to achieve a specific coverage distribution of a field. Such a distribution defines the relative importance of different field locations. We define the Constrained Mobility Coordination problem for Preferential Coverage (CMC-PC) as follows: given a field with a desired monitoring distribution, and a number of nodes n, each with its own schedule, we need to coordinate the mobility of the nodes in order to achieve the following two goals: 1) satisfy the schedules of all nodes, and 2) attain the required coverage of the given field. We show that the CMC-PC problem is NP-complete (by reduction to the Hamiltonian Cycle problem). Then we propose TFM, a distributed heuristic to achieve field coverage that is as close as possible to the required coverage distribution. We verify the premise of TFM using extensive simulations, as well as taxi logs from a major metropolitan area. We compare TFM to the random mobility strategy -the latter provides a lower bound on performance. Our results show that TFM is very successful in matching the required field coverage distribution, and that it provides, at least, two-fold query success ratio for queries that follow the target coverage distribution of the field.
Routing protocols in wireless sensor networks (WSN) face two main challenges: first, the challenging environments in which WSN's are deployed negatively affect the quality of the routing process. Therefore, routing protocols for WSN's should recognize and react to node failures and packet losses. Second, sensor nodes are battery-powered, which makes power a scarce resource. Routing protocols should optimize power consumption to prolong the lifetime of the WSN. In this paper, we present a new adaptive routing protocol for WSN's, we call it M 2 RC. M 2 RC has two phases: mesh establishment phase and data forwarding phase. In the first phase, M 2 RC establishes the routing state to enable multipath data forwarding. In the second phase, M 2 RC forwards data packets from the source to the sink. Targeting hop-by-hop reliability, an M 2 RC forwarding node waits for an acknowledgement (ACK) that its packets were correctly received at the next neighbor. Based on this feedback, an M 2 RC node applies multiplicative-increase/additive-decrease (MIAD) to control the number of neighbors targeted by its packet broadcast. We simulated M 2 RC in the ns-2 simulator [4] and compared it to GRAB [1], Max-power, and Min-power routing schemes. Our simulations show that M 2 RC achieves the highest throughput with at least 10-30% less consumed power per delivered report in scenarios where a certain number of nodes unexpectedly fail.
Routing protocols in wireless sensor networks (WSN) face two main challenges: first, the challenging environments in which WSN's are deployed negatively affect the quality of the routing process. Therefore, routing protocols for WSN's should recognize and react to node failures and packet losses. Second, sensor nodes are battery-powered, which makes power a scarce resource. Routing protocols should optimize power consumption to prolong the lifetime of the WSN. In this paper, we present a new adaptive routing protocol for WSN's, we call it M 2 RC. M 2 RC has two phases: mesh establishment phase and data forwarding phase. In the first phase, M 2 RC establishes the routing state to enable multipath data forwarding. In the second phase, M 2 RC forwards data packets from the source to the sink. Targeting hop-by-hop reliability, an M 2 RC forwarding node waits for an acknowledgement (ACK) that its packets were correctly received at the next neighbor. Based on this feedback, an M 2 RC node applies multiplicative-increase/additive-decrease (MIAD) to control the number of neighbors targeted by its packet broadcast. We simulated M 2 RC in the ns-2 simulator [4] and compared it to GRAB [1], Max-power, and Min-power routing schemes. Our simulations show that M 2 RC achieves the highest throughput with at least 10-30% less consumed power per delivered report in scenarios where a certain number of nodes unexpectedly fail.
We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.
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