Autonomic schemes for threat mitigation in Internet of Things

Enabling the ambient intelligence vision means that consumers will be provided with universal and immediate access to available content and services, together with ways of effectively exploiting them. Concentrating on the software system development aspect, this means that the actual implementation of any ambient intelligence application requested by a user can only be resolved at runtime according to the user's specific situation. This paper introduces a base declarative language and associated core middleware, which supports the abstract specification of Ambient Intelligence applications together with their dynamic composition according to the environment. The proposed solution builds on the Web services architecture, whose pervasiveness enables both services availability in most environments, and specification of applications supporting automated retrieval and composition. In addition, dynamic composition of applications is dealt in a way that enforces the quality of service of deployed applications in terms of security and performance.

/pdf/developing-ambient-intelligence-systems-a-solution-based-on-7to8bazqyb.pdf

Developing Ambient Intelligence Systems: A Solution based on Web Services

In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense subtructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter $D$, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that ($2 + \epsilon$)-approximate the densest subgraph and ($3 + \epsilon$)-approximate the at-least-$k$-densest subgraph (for a given parameter $k$). Our algorithms work for a wide range of parameter values and run in $O(D\log_{1+\epsilon} n)$ time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-$k$-densest subgraph problems for static distributed graphs.

pdf/dense-subgraphs-on-dynamic-networks-525bzag96f.pdf

Dense Subgraphs on Dynamic Networks

Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only $O(\log^2 n)$ memory, and are thus, compact schemes. 
We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only $O(\log n)$ local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwick's tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only $O(1)$ time and $\Delta$ messages, with only +3 degree increase and $O(log \Delta)$ graph diameter increase, over any sequence of deletions ($\Delta$ is the initial maximum degree). 
Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional $O(y \log \Delta)$ latency, where $y$ is the number of nodes that have been deleted on the path between $s$ and $t$. If $t$ has been deleted, $s$ gets informed and the packet removed from the network.

Compact Routing Messages in Self-Healing Trees

An implementation of Dynamic Source Routing on 802.15.4 using XBee Series 1 modules is presented. This implementation demonstrates the use of Dynamic Source Routing to determine the route from initiator (source) node to target (destination) node and used it to deliver message packets within an intra-network of wireless motes. The wireless motes compromise of Atmega 328P based microcontroller board (Red Board) interfaced with XBee Series 1. The algorithm itself searches for the desired route based on first come first serve basis and uses it to forward the message packet to the target node. Due to the dynamic nature of the protocol, the network has self-healing ability. The software library developed in the course of this implementation provides the user an interface to implement customized multi-hopping on XBee Series1 due to absence of any underlying operating system.

Implementation of Dyamic Source Routing using 802.15.4 on XBee Series 1 modules

The obvious advantage to wireless communication over wired is, as they say in the real estate business, location, location, location. Individuals and industries choose wireless because it allows flexibility of location--whether that means mobility, portability, or just ease of installation at a fixed point. The challenge of wireless communication is that, unlike the mostly error-free transmission environments provided by cables, the environment that wireless communications travel through is unpredictable. Environmental radio-frequency (RF) "noise" produced by powerful motors, other wireless devices, microwaves--and even the moisture content in the air--can make wireless communication unreliable.

Self-Healing Networks: Wireless networks that fix their own broken communication links may speed up their widespread acceptance.

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Self-Healing Networks: Wireless networks that fix their own broken communication links may speed up their widespread acceptance.