In particular types of Delay-Tolerant Networks (DTN) such as Opportunistic Mobile Networks, node connectivity is transient. For this reason, traditional routing mechanisms are no longer suitable. New approaches use social relations between mobile users as a criterion for the routing process. We argue that in such an approach, nodes with high social popularity may quickly deplete their energy resources -and, therefore, might be unwilling to participate in the routing process. We show that social-based routing algorithms such as BUBBLE Rap are prone to this behavior, and introduce energy awareness as an important criterion in the routing decision. We present experimental results showing that our approach delivers performances similar to BUBBLE Rap, whilst balancing the energy consumption between nodes in the network.

Monitoring crowds is receiving much attention. An increasingly popular technique is to scan mobile devices, notably smartphones. We take a look at scanning such devices based on transmitted WiFi messages. Although research on capturing crowd patterns using WiFi detections has been done, there are not many published results when it comes to tracking movements. This is not surprising when realizing that the data provided by WiFi scanners is susceptible to many seemingly erroneous and missed detections, caused by the use of randomized network addresses, overlap between scanners, high variance in WiFi detection ranges, among other sources. In this paper, we investigate various techniques for cleaning up sets of raw detections to sets that can subsequently be used for crowd analytics. To this end, we introduce two different quality metrics to measure the effects of applying various data filters. We test our approach using a data set collected from 27 WiFi scanners spread across the downtown area of a Dutch city where at that time a 3-day multi-stage festival took place attended by some 130,000 people.

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Internet of Things (IoT) represents a new paradigm in computing in which devices are connected to the internet and directly communicate with each other. Because these devices are generally thought to be wireless, small and cheap, in other words not very reliable, it is vital that we address the robustness problems in IoT.Applying standard fault tolerance models in IoT is impossible. The devices are not only heterogeneous, but unlike compute nodes, different devices have completely different capabilities and serve completely different functions (they have different sensors).We propose a model in which we describe the capabilities of each device and use this information to dynamically replace faulty devices with other, not-directly-compatible ones. Furthermore, our model uses the overlap between device characteristics in order to temporarily disable part of them and preserve energy. We show how the model can be applied on an IoT home security system, where robustness is critical. To offer a concrete example, a system based on our model would use a WiFi scanner, a heat sensor and a door opening sensor in order to replace a faulty security camera.

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