New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way. To set up distributed deep learning involves alterations of a great number of the parameter configurations of network/edge devices and DNN models, which are crucial to achieve best performances. Simulations measure scalability of intelligence applications in the early stage, as well as to determine the effects of different configurations, thus highly desired. However, work on simulating the distributed intelligence environment is still in its infancy. The existing simulation frameworks, such as NS-3, etc., cannot extended in a straightforward way to support simulations of distributed learning. In this paper, we propose a novel discrete event simulator, sim4DistrDL, which includes a deep learning module and a network simulation module to facilitate simulation of DNN-based distributed applications. Specifically, we give the design and implementation of the proposed learning simulator and present an illustrative use case.

Sensor nodes deployed outdoors are subject to environmental detriments and often need to cache data for an extended period of time. This paper introduces sensor nodes which are robust to environmental damages, and proposes to utilize Network Coding to back up data in the robust sensors for future data retrieval in an energy efficient way. Our goal is to help regular sensors select robust sensors to back up their data with low energy consumption, such that when needed, all the data can be retrieved by querying only a subset of robust sensors. We formally formulate this backup problem, theoretically prove its NP-Completeness, discover two novel theoretical guidelines for problem solving, and propose two algorithms accordingly to tackle this NP-C problem. The guidelines are based on random linear network coding and provide lower bounds of the number of robust sensors that each regular sensor should choose for data backup, such that the required fault tolerance is provided. A centralized algorithm and a distributed algorithm are developed based on the guidelines such that regular sensors can back up their data efficiently. Both analysis and simulation show our algorithms are effective in achieving fault tolerance, low energy consumption, and high retrieval efficiency.

Sensor nodes deployed outdoors for field surveillance are subject to environmental detriments. In this article, we propose a heterogeneous sensor network composed of sensor nodes with different environmental survivability to make it robust to environmental damage and keep it at a reasonable cost. We, for the first time, study the scheduling problem in such heterogeneous sensor networks for critical location surveillance applications. Our goal is to monitor all the critical points for as long as possible under different environmental conditions. We identify the underlying problem, theoretically prove its NP-complete nature, and propose a novel adaptive greedy scheduling algorithm to solve the problem. The algorithm incorporates several heuristics to schedule the activity of both regular and robust sensors to monitor all the critical points, while at the same time minimizing and balancing the network energy consumption. Simulation results show that our algorithm efficiently solves the problem and outperforms other alternatives.

Nowadays, distracted driving is becoming a very dangerous epidemic on the roadways. A lot of activities may lead to distracted driving, such as texting, making phone calls, using GPS or road maps, eating, using in-car entertainment systems, etc. As the number of smartphones is rapidly growing year by year, using smartphone is by far the most dangerous and alarming driver distraction. In this paper, we design an app on iPhone for reducing the smartphone-related distracted driving, which can run in the background and can lock the smartphone screen with no passwords required when it detects that the user is driving. After the user finishes driving, the lock will be removed immediately. We also describe a DTM algorithm to detect the transportation mode and give the performance analysis of our system. The results show that our system can save more energy than other related applications.

Existing multipath routing protocols for MANET ignore the topology-exposure problem. This paper analyzes the threat of topology-exposure and proposes a Topology-Hiding Multipath Routing protocol (THMR). THMR doesn't allow packets to carry routing information, so malicious nodes cannot deduce topology information and launch various attacks based on that. The protocol can also establish multiple node-disjoint routes in a route discovery attempt and exclude unreliable routes before transmitting packets. We formally prove that THMR is loop-free and topology-hiding. Simulation results show that our protocol has better capability of finding routes and can greatly increase the capability of delivering packets in the scenario where there are attackers at the cost of low routing overhead.

Hierarchical architectures are more and more widely adopted for organizing wireless sensor networks. In such architectures, middle-tier nodes take important roles, and preventing a malicious node from impersonating a middle-tier node and injecting falsified messages becomes critical. In this paper, we propose an energy efficient, distributed scheme to secure the multicast messages from the middle-tier nodes. Our scheme does not require a priori knowledge about the hierarchical relation between middle-tier nodes and lowest-tier nodes, and is adaptive to changes of this relation. Extensive simulations are conducted to evaluate our scheme, and the results show that the scheme is energy efficient.