Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-receptionwithin-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. We identify and illustrate this weak-link problem and the related distancehop trade-off, whereby energy efficient geographic forwarding must strike a balance between shorter, high-quality links, and longer lossy links. The study is done for scenarios with and without automatic repeat request (ARQ).Based on an analytical link loss model, we study the distance-hop trade-off via mathematical analysis and extensive simulations of a wide array of blacklisting/link-selection strategies; we also validate some strategies using a set of real experiments on motes. Our analysis, simulations and experiments all show that the product of the packet reception rate (PRR) and the distance traversed towards destination is the optimal forwarding metric for the ARQ case, and is a good metric even without ARQ. Nodes using this metric often take advantage of neighbors in the transitional region (high-variance links). Our results also show that receptionbased forwarding strategies are more efficient than purely distance-based strategies; relative blacklisting schemes reduce disconnections and achieve higher delivery rates than absolute blacklisting schemes; and that ARQ schemes become more important in larger networks.

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance.In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

In the absence of location errors, geographic routing -using a combination of greedy forwarding and face routing -has been shown to work correctly and efficiently. The effects of location errors on geographic routing have not been studied before. In this work we provide a detailed analysis of the effects of location errors on the correctness and performance of geographic routing in static sensor networks. First, we perform a micro-level behavioral analysis to identify the possible protocol error scenarios and their conditions and bounds. Then, we present results from an extensive simulation study of GPSR and GHT to quantify the performance degradation due to location errors. Our results show that even small location errors (of 10% of the radio range or less) can in fact lead to incorrect (non-recoverable) geographic routing with noticeable performance degradation. We then introduce a simple modification for face routing that eliminates probable errors and leads to near perfect performance.