SenseLE:Exploiting spatial locality in decentralized sensing environments

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%

PeerMatcher: Decentralized Partnership Formation

Abstract-This paper presents PeerMatcher, a fully decentralized algorithm solving the k-clique matching problem. The aim of k-clique matching is to cluster a set of nodes having pairwise weights into k-size groups of maximal total weight. Since solving the problem requires exponential time, PeerMatcher employs a novel set of heuristics that aim at converging to the optimal grouping while keeping the associated time and computational complexity low. A key feature is the use of peerto-peer communication. An extensive evaluation of PeerMatcher demonstrates its accuracy, efficiency, and scalability

RideMatcher:Peer-to-peer matching of passengers for efficient ridesharing

The daily home-office commute of millions of people in crowded cities puts a strain on air quality, traveling time and noise pollution. This is especially problematic in western cities, where cars and taxis have low occupancy with daily commuters. To reduce these issues, authorities often encourage commuters to share their rides, also known as carpooling or ridesharing. To increase the ridesharing usage it is essential that commuters are efficiently matched. In this paper we present RideMatcher, a novel peer-to-peer system for matching car rides based on their routes and travel times. Unlike other ridesharing systems, RideMatcher is completely decentralized, which makes it possible to deploy it on distributed infrastructures, using fog and edge computing. Despite being decentralized, our system is able to efficiently match ridesharing users in near real-time. Our evaluations performed on a dataset with 34,837 real taxi trips from New York show that RideMatcher is able to reduce the number of taxi trips by up to 65%, the distance traveled by taxi cabs by up to 64%, and the cost of the trips by up to 66%