Abstract-We have developed SmartConnect, a tool that addresses the growing need for the design and deployment of multihop wireless relay networks for connecting sensors to a control center. Given the locations of the sensors, the traffic that each sensor generates, the quality of service (QoS) requirements, and the potential locations at which relays can be placed, SmartConnect helps design and deploy a lowcost wireless multihop relay network. SmartConnect adopts a field interactive, iterative approach, with model based network design, field evaluation and relay augmentation performed iteratively until the desired QoS is met. The design process is based on approximate combinatorial optimization algorithms. In the paper, we provide the design choices made in SmartConnect and describe the experimental work that led to these choices. We provide results from some experimental deployments. Finally, we conduct an experimental study of the robustness of the network design over long time periods (as channel conditions slowly change), in terms of the relay augmentation and route adaptation required.Index Terms-Wireless sensor network design; Wireless relay network design and deployment; Field interactive design I. Introduction Industrial and commercial establishments (such as chemical factories and hotels) deploy a large number of sensors for control or monitoring applications. The sensors are typically spread over a large area and at distances of several tens of meters from the control center. In existing installations, the sensors are connected to the control center by a wireline network, usually a combination of point-to-point and bus networks. Installation and maintenance of such wireline networks incur substantial cost. In addition, it is difficult to expand such wireline sensor networks, for example, to add sensors at some new locations. Due to such reasons, recently there has been a spurt of interest in replacing wireline sensor networks with multihop wireless sensor networks.There are several sensing applications, particularly in industrial settings, that could employ low power wireless sensors that use the wireless physical (PHY) layers and medium access controls (MAC) being standardized by IEEE 802.15.4 [14], or Wireless HART [3], or ISA 100.11a [4]. Such low power devices can simply be "planted" where needed, and can be expected to work for several months on batteries and harvested energy. Due to their low power operation, the range of such radios is a few meters to a few 10s of meters, necessitating multihopping, and therefore a higher packet loss rate. There are many applications, however, e.g., such as data logging and non-critical control (see [18]), for
We are motivated by the problem of impromptu or asyou-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.
We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that number of hops in the path from each sensor to its BS is bounded by hmax, and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios.Index Terms-Wireless sensor network design; Multiple sink and relay placement; QoS-aware network design
Information centric networking (ICN) is a proposal for a future internetworking architecture that is more efficient and scalable. While several ICN architectures have been evaluated for networks carrying web and video traffic, the benefits and challenges it poses for Internet of Things (IoT) networks are relatively unexplored. In our work, we evaluate the performance implications for typical IoT network scenarios in the ICN paradigm. We study the behavior of innetwork caching, introduce a way to make caching more efficient for periodic sensor data, and evaluate the impact of presence and location of lossy wireless links in IoT networks. In this paper, we present and discuss the results of our evaluations on IoT networks performed through emulations using a specific ICN architecture, namely, content centric networking (CCN). For example, we show that the newly proposed UTS-LRU cache replacement strategy for improved caching performance of time series content streams reduces the number of messages transmitted by up to 16%. Our findings indicate that the performance of IoT networks using ICN are influenced by the content model and the nature of its links, and motivates further studies to understand the performance implications in more varied IoT scenarios.
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