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Abstract-Underwater Sensor Networks are typically distributed in nature and the nodes communicate using acoustic waves over a wireless medium. Such networks are characterized by long and variable propagation delays, intermittent connectivity, limited bandwidth and low bit rates. Due to the wireless mode of communication between the sensor nodes, a Medium Access Control (MAC) protocol is required to coordinate access to the shared channel and enable efficient data communication. However, conventional terrestrial wireless network protocols that are based on RF technologies cannot be used underwater. In this paper, we propose PLAN -a MAC Protocol for Long-latency Access Networks that is designed for use in half-duplex underwater acoustic sensor networks. We utilize CDMA as the underlying multiple access technique, due to its resilience to multi-path and Doppler's effects prevalent in underwater environments, coupled with an RTS-CTS handshaking procedure prior to the actual data transmission. Using simulations, we study the performance and efficiency of the proposed MAC protocol in underwater acoustic networks.

Indoor maps, as crucial prerequisites for many indoor localization and navigation systems, are sometimes inaccessible. The absence of an indoor map database and the high cost of manually constructing an indoor map produce a need for an inexpensive and efficient way to dynamically construct indoor maps. The ubiquity of sensor-equipped mobile devices enables us to crowdsource user trajectories, out of which indoor digital maps can be automatically constructed at low costs. Similar to other crowdsourced data, the collected user trajectories are often noisy and of low fidelity, which poses a challenge to the accurate map construction. To alleviate this problem, we propose CIMLoc-a crowdsourcing indoor map construction system for localization. The system is evaluated with real-world trajectories collected from different mobile devices. We quantify the construction errors by computing the localization errors achieved with the constructed map and the real map. Experimental results reveal that CIMLoc is able to construct accurate maps that significantly improve localization results. We believe that CIMLoc provides an effective solution to the indoor localization problems where the indoor maps are unavailable.

Abstract-Upon the occurrence of a phenomenon of interest in a wireless sensor network, multiple sensors may be activated, leading to data implosion and redundancy. Data aggregation and/or fusion techniques exploit spatio-temporal correlation among sensory data to reduce traffic load and mitigate congestion. However, this is often at the expense of loss in Information Quality (IQ) of data that is collected at the fusion center.In this work, we address the problem of finding the leastcost routing tree that satisfies a given IQ constraint. We note that the optimal least-cost routing solution is a variation of the classical NP-hard Steiner tree problem in graphs, which incurs high overheads as it requires knowledge of the entire network topology and individual IQ contributions of each activated sensor node. We tackle these issues by proposing: (i) a topology-aware histogram-based aggregation structure that encapsulates the cost of including the IQ contribution of each activated node in a compact and efficient way; and (ii) a greedy heuristic to approximate and prune a least-cost aggregation routing path. We show that the performance of our IQ-aware routing protocol is: (i) bounded by a distance-based aggregation tree that collects data from all the activated nodes; and (ii) comparable to another IQ-aware routing protocol that uses an exhaustive brute-force search to approximate and prune the least-cost aggregation tree.