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The identification of a favorable location for investment is a key aspect influencing the real estate market of a smart city. The number of factors that influence the identification easily runs into a few hundreds (including floor space area, crime in the locality and so on). Existing literature predominantly focuses on the analysis of price trends in a given location. This paper aims to develop a set of tools to compute an optimal location for investment, a problem which has received little attention in the literature (analysis of house price trends has received more attention). In previous work the authors proposed a machine learning approach for computing optimal locations. There are two main issues with the previous work. All real estate factors were assumed to be independent and identically distributed random variables. To address this, in the current paper we propose a network structure to derive the relational inferences between the factors. However, solving the location identification problem using only a network incurs computational burden. Hence, the machine learning layers from the previous work is combined with a network layer for computing an optimal location with proven lower computational cost. A second issue is that the computations are performed on an online database which has inherent privacy risks. The online data, user information and the algorithms can be tampered through privacy breaches. We present a privacy preservation technique to protect the algorithms, and use blockchains to secure the identity of the user. This paper presents solutions to two interesting problems in the analysis of real estate networks: a) to design tools that can identify an optimal location for investment and b) to preserve the privacy of the entire process using privacy preserving techniques and block chains.

Bitcoin is a popular cryptocurrency that records alltransactions in a distributed append-only public ledger calledblockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus pro-tocol, which is run by network nodes called miners. In exchangefor the incentive, the miners are expected to honestly maintainthe blockchain. Since its launch in 2009, Bitcoin economy hasgrown at an enormous rate, and it is now worth about 170 billions of dollars. This exponential growth in the market valueof Bitcoin motivates adversaries to exploit weaknesses for profit,and researchers to discover new vulnerabilities in the system,propose countermeasures, and predict upcoming this http URL this paper, we present a systematic survey that covers thesecurity and privacy aspects of Bitcoin. We start by presenting anoverview of the Bitcoin protocol and its major components alongwith their functionality and interactions within the system. Wereview the existing vulnerabilities in Bitcoin and its underlyingmajor technologies such as blockchain and PoW based consensusprotocol. These vulnerabilities lead to the execution of varioussecurity threats to the normal functionality of Bitcoin. Wethen discuss the feasibility and robustness of the state-of-the-art security solutions. Additionally, we present current privacyand anonymity considerations in Bitcoin and discuss the privacy-related threats to Bitcoin users along with the analysis of theexisting privacy-preserving solutions. Finally, we summarize thecritical open challenges and suggest directions for future researchtowards provisioning stringent security and privacy techniquesfor Bitcoin.

Security is of a prime importance in Wireless Sensor Networks (WSN). Because of less human intervention and selfmanagement aspects of these types of networks, they are often prone to security threats and vulnerabilities. In such network scenario, we propose genetics based random key distribution scheme for securing clustered WSNs. The algorithm is simulated in MATLAB. The obtained result proves that the proposed method is energy efficient than other widely used cryptographic techniques and using these types of bioinspired intelligences provides robust security in the network.

Clustering protocols have proven to increase the network throughput, reduce delay in packet transfer and save energy. Hence, in this work, we propose a novel clustering protocol that uses firefly algorithm inspired approach towards improving the existing basic LEACH protocol for reduction in steady-state energy consumption, aiming to enhance the network lifetime. The simulated results prove that implanting these kinds of computational intelligence into the pre-existing protocols considerably improves its performance.

Wireless Sensor Networks (WSNs) are often prone to risk of security attacks and vulnerabilities. This is because of the less human intervention in their operations. Hence, novel security mechanisms and techniques are of a prime importance in these types of networks. In this context, we propose a unique security scheme, which coalesce the random keying technique with memetics. The application of these kinds of bio-inspired computation in WSNs provides robust security in the network with the obtained results supporting the security concerns of the network.