Day by day cities become more intelligence because governments move slowly to convert each thing to become smarter. These cities are built with the goal of increasing liveability, safety, revivification, and sustainability by building smart services like smart education, smart government, smart mobility, smart homes and e-health but it is important to build these services along with the method for securing and maintaining the privacy of citizen’s data. Citizens can build their own services that meet their requirements and needs. This book chapter discusses the internet of things and its applications in smart cities then discusses smart cities and challenge that faces smart cities and describes how to protect citizen data by securing the WiFi based data transmission system that encrypts and encodes data before transfer from source to destination where the data is finally decrypted and decoded. The proposed system is embedded with authentication method to help the authorized people to access the data. The proposed system first compresses data with run-length encoding technique then encrypt it using the AES method but with a rotated key then the source transfers the encoded and encrypted data to the destination where the data is decrypted then decoded to restore the original data then the original data is upload to the destination’s website.

Data Security and Challenges in Smart Cities

Object serialization is important to a variety of applications, including session migration and distributed computing. A general JavaScript object serializer must support function serialization as functions are first-class objects. However, JavaScript offers no built-in function serialization and limits custom serializers by exposing no meta operator to query a function’s captured variables. Code instrumentation can expose captured variables but state-of-the-art instrumentation techniques introduce high overheads, vary in supported syntax and/or use complex (de)serialization algorithms. We introduce FlashFreeze, an instrumentation technique based on capture lists. FlashFreeze achieves a tiny run time overhead: an Octane score reduction of 3% compared to 76% for the state-of-the-art ThingsMigrate tool and 1% for the work-in-progress FSM tool. FlashFreeze supports all self-contained ECMAScript 5 programs except for specific uses of eval, with, and source code inspection. FlashFreeze’s construction gives rise to simple (de)serialization algorithms.

FlashFreeze: low-overhead JavaScript instrumentation for function serialization

Web App migration means capturing a snapshot of the execution state of an web app on a device, and restoring it on another device to continue its execution, for cross-device liquid computing. Although web apps are relatively easy to migrate due to its high portability, there is a JavaScript language feature called closure, which complicates migration since it requires migrating the variable states of already-finished outer functions. One approach of web app migration is to instrument the source code to trace the closure variables, yet it often suffers from performance slowdown, especially for multiple migrations. In this paper, we propose a new instrumentation-based technique called Disclosure, which moves the declarations of closure variables to a managed data structure and replaces closure variables by the corresponding references to the data structure. This can improve the runtime performance while enhancing security. We evaluated our work with eight Octane benchmarks and four real web apps. The runtime performance penalty due to Disclosure is 0%–15%, which is much better than the result of the latest instrumentation-based work that supports deep closures and multiple migrations, as Disclosure. Real web apps are also shown to migrate seamlessly, even multiple times. 

Disclosure: Efficient Instrumentation-Based Web App Migration for Liquid Computing

Ever since the need for new approaches and systems to handle data streams was identified in early 2000s, stream processing has been an active area of research, resulting in a large body of work with significant impact.This tutorial reflects on this research history by highlighting a number of trends and best practices that can be identified in hindsight. It also enumerates a list of directions for future research in stream processing.

Reflections on Almost Two Decades of Research into Stream Processing: Tutorial

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. Unlike traditional embedded devices with specialized firmwares, modern IoT devices accommodate general‐purpose operating systems, allowing developers to run more sophisticated applications written in high‐level languages like JavaScript. Because IoT devices are subject to resource constraints like available battery power, we need to dynamically migrate a running process between different devices to prevent losing state. However, it is challenging to apply migration techniques using memory snapshots across the heterogeneous pool of IoT devices. We present ThingsMigrate, a middleware providing platform‐independent migration of JavaScript processes across IoT devices. Prior to execution, ThingsMigrate instruments the source code of a given program to expose its internal state. During run‐time, the transformed program produces on demand a JSON snapshot of its current state, from which new code is generated to resume execution. Thus, ThingsMigrate enables process migration entirely in the application space without any modifications to the underlying virtual machine (VM), providing VM‐independence. We present three versions of ThingsMigrate, each building on the previous to optimize for run‐time latency and memory consumption. We report on the experience of building each successive version and discuss the insights gained and the learning outcomes. We evaluated ThingsMigrate against standard benchmarks, over two IoT platforms and a cloud‐like environment. We show that it can migrate even highly CPU‐intensive applications, with average run‐time latency overhead of 33% and memory overhead of 78%. ThingsMigrate supports multiple subsequent migrations without introducing additional overhead over each subsequent migration.

ThingsMigrate: Platform‐independent migration of stateful JavaScript Internet of Things applications

The connected car is likely to play a fundamental role in the foreseeable Internet of Things. The connectivity aspect in combination with the available data (e.g. from GPS, on-board diagnostics, road sensors) and video (e.g. from dashcams and traffic cameras) streams enable a range of new applications, e.g., accident avoidance, online route planning, energy optimization, etc.These applications, however, come with an additional set of requirements which are not accommodated by the state-of-the-art stream processing platforms. We have built World-Wide Streams (WWS), a novel stream processing platform that has been explicitly designed with those requirements in mind. In this demo presentation, we will show a number of connected car scenarios that we have built on top of WWS.

Building Connected Car Applications on Top of the World-Wide Streams Platform: Demo

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Building Connected Car Applications on Top of the World-Wide Streams Platform: Demo

FlashFreeze: low-overhead JavaScript instrumentation for function serialization