With the current advancements in Internet of Things (IoT) technology, which offers connectivity of the physical world with the sophisticated information processing world, the utopian vision of Smart Cities now appears to be a close reality. This, however, opens the possibility of security breaches of data at various levels, from information collection, to processing, and finally, at the actuation. Due to the presence of low-power devices as information collectors and actuators in the IoT, physical layer security is a promising solution for privacy-aware Smart Cities because of its low implementation cost, and relatively low complexity compared with computation-intensive cryptographic techniques. In this chapter, we first introduce the physical layer architectures suitable for the IoT and discuss the technological challenges associated with them. Building upon them, we present novel optimization techniques and smart green cooperative protocols for maximizing security. We conclude the chapter by discussing open research directions that need further investigation for realizing energy-efficient secure Smart Cities.

Privacy-Aware Physical Layer Security Techniques for Smart Cities

Graph entropy measures have recently gained wide attention for identifying and discriminating various networks in biology, society, transportation, etc. However, existing methods cannot sufficiently explore the structural contents by merely considering the elementary invariants of a graph, ignoring the underlying patterns in higher-order features. In this paper, we propose a general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entropy-based graph representation framework</i> ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Greet</small> ) based on four pertinent properties of graphlet topology from urelement to higher-order statistics. Specifically, we introduce an unbiased graphlet estimation strategy for obtaining both urelement and higher-order statistics. Additionally, we define a novel family of information functions based on hierarchical topological features to compute the graph entropy, then construct a graph information entropy (GIE) vector using the obtained local and global structural statistics to facilitate downstream tasks. Furthermore, there are some advantages that our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Greet</small> exhibits over other methods: (a) high accuracy with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; 1\%$</tex-math></inline-formula> relative error; (b) scalable for even larger vertex graphlets; (c) efficient calculation procedure with feasible speedup. Extensive experiments show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Greet</small> exhibits superior performance on graph classification and clustering tasks, achieving remarkable improvements compared to several baselines. Altogether these findings pave the way for a wide range of applications of graphlet-based entropy as a complexity metric in graph analysis. 

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A Graph Entropy Measure From Urelement to Higher-Order Graphlets for Network Analysis

Graph entropy measures have recently gained wide attention for identifying and discriminating various networks in biology, society, transportation, etc. However, existing methods cannot sufficiently explore the structural contents by merely considering the elementary invariants of a graph, ignoring the underlying patterns in higher-order features. In this paper, we propose a general entropy-based graph representation framework (Greet) based on four pertinent properties of graphlet topology from urelement to higher-order statistics. Specifically, we introduce an unbiased graphlet estimation strategy for obtaining both urelement and higher-order statistics. Additionally, we define a novel family of information functions based on hierarchical topological features to compute the graph entropy, then construct a graph information entropy (GIE) vector using the obtained local and global structural statistics to facilitate downstream tasks. Furthermore, there are some advantages that our Greet exhibits over other methods: (a) high accuracy with $< 1\%$ relative error; (b) scalable for even larger vertex graphlets; (c) efficient calculation procedure with feasible speedup. Extensive experiments show that Greet exhibits superior performance on graph classification and clustering tasks, achieving remarkable improvements compared to several baselines. Altogether these findings pave the way for a wide range of applications of graphlet-based entropy as a complexity metric in graph analysis.

This letter presents a small-scale wireless distributed cooperative secure-communication network (WDCSN) design using a novel spectral graph FIR filter to achieve desired secrecy capacity at the intended receiver in the presence of single and multiple eavesdroppers. An undirected weighted graph $\mathcal {G} (V, \mathcal {E}, W)$ , with relays, eavesdroppers, source, and destination are as nodes ( $V$ ), and circularly symmetric complex Gaussian random variables as edge weights ( $W$ ) are considered to represent the WDCSN. A graph Laplacian is used as a graph shift operator to design the proposed filter. Filter coefficients are calculated by using the least squared error as a criterion. Simulations are conducted for a two-way WDCSN to achieve the desired secrecy capacity using different graph structures considered for WDCSN. The proposed filter design's performance is quantified by using the secrecy outage probability metric. The results show that desired secrecy capacity with SOP of $10^{-2}$ is achievable using the proposed methodology, irrespective of graph structures with variable complexity. An SOP performance gain of $(60\!-\!80) \%$ is achieved over the SOP reported in the literature.

A Small-Scale Wireless Distributed Cooperative Secure Communication Network Design Using Graph FIR Filters

Prominent requirements such as high data rate demands across weaker coverage areas have lead to the utilization of cooperative communication system in fifth generation (5G) networks. In this regard, this paper presents a novel joint transmit power sharing and relay placement scheme for decode-and-forward relay assisted secure communication to a legitimate user in the presence of an untrusted user. Observing that the joint secure rate maximization problem for the trusted user is non-convex, first we present key insights on optimal power sharing between source and relay, and then derive an equivalent single variable problem on relay placement. Next, tight analytical bounds for optimal relay placement are discoursed to ultimately come up with a computationally efficient joint global optimization algorithm. Lastly, the selected numerical results validate the analysis and highlight the substantial gains achieved by the proposed joint design over benchmark scheme.

Secrecy-Aware Jointly Optimal Transmit Power Budget Sharing and Trusted DF Relay Placement

In resource allocation for secure cooperative communication systems, along with power allocation, relay placement also has equal importance in improving the system performance. In this paper, we study the joint optimization of relay placement and power allocation to minimize the secrecy outage probability (SOP) for a four- node cooperative system with a trusted randomize- and-forward relay in the presence of an external eavesdropper. Initially, we derive the expression of SOP, and then formulate an optimization problem to minimize SOP under given power budget and relay placement (RP) constraints. By providing analytical insights on power allocation (PA) between source and relay, we obtain closed form expressions of optimal PA (OPA) for a given RP that minimizes the SOP. Next, we propose a low complexity algorithm to obtain the near-optimal RP (ORP) for a given PA. Finally, we obtain optimal SOP with joint RP and PA. Numerical results present validation of analytical SOP through Monte Carlo simulations, optimal PA for a given RP, optimal RP for a given PA, and joint PA and RP for a given secrecy threshold rate. Finally, we highlight the significant performance gains achieved by the joint design over the conventional scheme.

Optimizing Secrecy Performance of Trusted RF Relay against External Eavesdropping

This paper presents the secrecy performance analysis of an amplify-and-forward relay-assisted cooperative communication system in the presence of a passive external eavesdropper. In contrast to existing works that assume high signal-to-noise ratio (SNR) approximations, we have investigated exact and secrecy outage probabilities. Furthermore, we consider a more challenging scenario where the source may not be reachable to the intended user directly. But the eavesdropper can tap both the source link and the relay link. First of all, the outage probability is analyzed at the intended user as well as the eavesdropper. Next, defining the secrecy rate for the amplify-and-forward (AF) relaying system, the expression of the secrecy outage probability (SOP) and the secrecy intercept probability (SIP) have been derived, respectively. Noticing the complexity involved in the integration of SOP and SIP expressions, the closed-form expressions have been derived for asymptotic cases. Finally, the exact and asymptotic analysis has been verified by performing Monte-Carlo simulations. It is observed that the relay position should be closer to the source compared to the eavesdropper to achieve improved SOP.

Kotha Venugopalachary

Papers

A Small-Scale Wireless Distributed Cooperative Secure Communication Network Design Using Graph FIR Filters

Secrecy-Aware Jointly Optimal Transmit Power Budget Sharing and Trusted DF Relay Placement

Optimizing Secrecy Performance of Trusted RF Relay against External Eavesdropping

Exact Outage Analysis for Non-regenerative Secure Cooperation Against Double-tap Eavesdropping