Fog Challenges In IoT

Although Fog computing is a promising network paradigm to serve IoT applications/systems, there are a number of challenging issues that need significant attention. Despite the fact that there are a number of research projects that have been conducted on fog computing, there are on-going research challenges and opportunities still open to discuss. The main recurring challenging issues of fog computing in the IoT are threefold:

• Heterogeneity: in IoT-based applications, the bottom most layer within the IoT (thing layer) can have multiple different devices such as smart-phones, autonomous cars, wristbands and other IoT smart objects. The heterogeneity issue emerge at this point due to the heterogeneous data-formats [91], which limits the data aggregation processes and thus could directly impact the QoS and QoE can be provided to the end users if data could not processed in time due to its heterogeneous nature. Deal- ings with various data formats and different communication protocols for managing unstructured data becomes a major issue. Heterogeneity becomes an substantial de- signing factor to be considered during the design phase of an IoT -F og based system architecture [92]. Therefore, this raises the issue of how fogs can handle various data formats and network protocols from highly dissimilar sources of data.

• Resource Management: when IoT layers (a.k.a, things, fogs, and clouds) are integrated into one network, the management of the resources becomes a primary concern [93]. Resource discovery and sharing are critical factors for IoT applications, as it could affect the services and QoS directly. Due to the dynamic nature of IoT nodes in terms of communication and data acquisition, significant challenges arise. Even when considering fog without the aid of the cloud [63, 64, 65], resource management can be challenging. This is due to the limited computing and storage resources available in the fog compared to the cloud. Fog resource management is not been widely studied in most existing researches studies [94, 26]. Thus, the question of “How to balance the

and power-reduction is an open research challenge. Understanding the nature of fog in the way it deals with data and the mobility of things may be beneficial for re- source management and task scheduling within fog to allow best QoS. In resource management, offloading can be a solution to balance the fog’s workload, however, it still experiences some issues [1, 39, 95, 96, 97], for example, the question of “when to offload a task?” in a way that can allow efficient resource management while insuring best QoS is still an open research challenge. Offloading refers to the transfer of tasks from one entity to another, such as one fog to another or to cloud. For example a fog node transferring the load to another node that’s experiencing less load. What makes offloading challenging is how tasks should be offloaded, and what reasons should be applicable to make the decision to offload the task(s), hence achieving minimal delay. To the best of our knowledge, there are a number of research studies tackling the challenges associated with task scheduling [26] at the fog layer. However, most pro- posed research so far permits distributing jobs over participant Fog nodes regardless of the current workload on nodes. In other words, they have not appreciated the possible unbalanced situation among fog nodes in terms of traffic and workloads [26]. In fact, most of the proposed algorithms focus on reduce the task blocking prob- ability, hence, they cause such unbalanced loads, among participant nodes. This stresses the need for algorithms and framework that support offloading [98, 99] and load redistribution [100] activities at the fog layer. Offloading could be detrimental to latency-sensitive systems if carried out in an unsuitable manner. If the offloading of the tasks causes more delay, it could reduce the QoS and QoE.

• Security (Threats and Attacks): security in fog computing is also a changing issue which can directly impacts the QoP and QoE to end users, threats and attacks are mainly because of fog’s geo-distribution and positioning within the network. Working at the network edge could present threats that do not exist within an organised cloud architecture. One of these threats could be a Denial-of-Service (DoS) attack, in which the attacker can relay and alter the communication between two parties [101], thus affecting the QoP. For example, in a healthcare system, the attack could compromise a gateway that is in between a patient monitoring sensor and a fog node that processes patient’s data, hence, a major consequence regarding the patient’s health occurs if

the attacker altered the data that being processed. Not only gateways are abused for attacks, but fog node themselves can also be attacked and manipulate them to become malicious fog nodes. A malicious fog nodes can disrupt network operations through various attacks, the following attacks [102, 103, 104] are considered since they can directly effect the reliability of fog computing.

1. Forgery:- malicious fog nodes may forge their identities and fabricate fake data to mislead other fog nodes and IoT services. This type of node burdens the network resources by excessively consuming network bandwidth, storage and computational power by running a fake services and fabricating large amounts of faked data.

2. Tampering:- malicious tampering with fog nodes degrades fog efficiency by de- laying, modifying or droping the transmitted data. Detecting such malicious fog nodes is difficult as transmission failure or delay may be caused by other factors, such as unstable channel conditions or weak network signal, and not due to tampering with the fog.

3. Spam and Jamming:- this attack burdens the network with unwanted content and data by generating large amount of bogus data to jam the network channels and the fog’s resources. Such attacks are generated and spread by malicious fogs to consume network and fog’s resources so that the fog become unavailable for other services and processes.

4. Impersonation: A malicious fog pretends to be a legitimate fog node to provides fog’s services, but then it provide fake or phishing services to users and breaches users privacy.

5. Denial of Service (DoS):- malicious attacks to disrupt the fog’s services and make them unavailable to the intended users, by flooding the target fog nodes with superfluous service requests. This attack consumes network resources to prevent the requests from legitimate users from being fulfilled. Fog nodes are highly vulnerable to DoS attacks compared to the cloud due to the fog’s lim- ited resources.